HUMAN-AI TEAMING
Lab

2020

  • Planning for Hybrid Systems via Satisfiability Modulo Theories. Michael Cashmore, Daniele Magazzeni and Parisa Zehtabi; Journal of Artificial Intelligence Research (JAIR), , (to appear), 2020.

    Abstract
    Abstract:
    Planning for hybrid systems is important for dealing with real-world applications, and PDDL+ supports this representation of domains with mixed discrete and continuous dynamics. In this paper we present a new approach for planning for hybrid systems, based on encoding the planning problem as a Satisfiability Modulo Theories (SMT) formula. This is the first SMT encoding that can handle the whole set of PDDL+ features (including processes and events), and is implemented in the planner SMTPlan. SMTPlan not only covers the full semantics of PDDL+, but can also deal with non-linear polynomial continuous change without discretization. This allows it to generate plans with non-linear dynamics that are correct-by-construction. The encoding is based on the notion of happenings, and can be applied on domains with nonlinear continuous change. We describe the encoding in detail and provide in-depth examples. We apply this encoding in an iterative deepening planning algorithm. Experimental results show that the approach dramatically outperforms existing work in finding plans for PDDL+ problems. We also present experiments which explore the performance of the proposed approach on temporal planning problems, showing that the scalability of the approach is limited by the size of the discrete search space. We further extend the encoding to include planning with control parameters. The extended encoding allows the definition of actions to include infinite domain parameters, called control parameters. We present experiments on a set of problems with control parameters to demonstrate the positive effect they provide to the approach of planning via SMT.
    @article{jariSMT_2020,
        author = "Cashmore, Michael and Magazzeni, Daniele and Zehtabi, Parisa",
        title = "{Planning for Hybrid Systems via Satisfiability Modulo Theories}",
        journal = "Journal of Artificial Intelligence Research (JAIR)",
        pages = "(to appear)",
        year = "2020"
    }

  • A New Approach to Plan-Space Explanation: Analyzing Plan-Property Dependencies in Oversubscription Planning. Rebecca Eiffer, Michael Cashmore, Joerg Hoffmann, Daniele Magazzeni and Marcel Steinmetz; Proceedings of AAAI Conference on Artificial Intelligence (AAAI 2020), 2020.

    Abstract
    Abstract:
    In many usage scenarios of AI Planning technology, users will want not just a plan pi but an explanation of the space of possible plans, justifying pi. In particular, in oversubscription planning where not all goals can be achieved, users may ask why a conjunction A of goals is not achieved by pi. We propose to answer this kind of question with the goal conjunctions B excluded by A, i.e., that could not be achieved were A enforced. We formalize this approach in terms of plan-property dependencies, where plan properties are propositional formulas over the goals achieved by a plan, and dependencies are entailment relations in plan space. We focus on entailment relations of the form A ==> not B, and devise analysis techniques globally identifying all such relations, or locally identifying the implications of a single given plan property (user question) A. We show how, via compilation, one can analyze dependencies between a richer form of plan properties, specifying formulas over action subsets touched by the plan. We run comprehensive experiments on adapted IPC benchmarks, and find that the suggested analyses are reasonably feasible at the global level, and become significantly more effective at the local level.
    @inproceedings{Eiffer_AAAI2020,
        author = "Eiffer, Rebecca and Cashmore, Michael and Hoffmann, Joerg and Magazzeni, Daniele and Steinmetz, Marcel",
        title = "{A New Approach to Plan-Space Explanation: Analyzing Plan-Property Dependencies in Oversubscription Planning}",
        booktitle = "Proceedings of AAAI Conference on Artificial Intelligence (AAAI 2020)",
        year = "2020",
        publisher = "AAAI",
        address = "",
        pages = "",
        isbn = "",
        doi = ""
    }

  • Let’s Learn their Language? A Case for Planning with Automata-Network Languages from Model Checking. Joerg Hoffmann, Holger Hermanns, Michela Klauck, Marcel Steinmetz, Erez Karpas and Daniele Magazzeni; Proceedings of AAAI Conference on Artificial Intelligence (AAAI 2020), 2020.

    Abstract
    Abstract:
    It is widely known that AI planning and model checking are closely related. Compilations have been devised between various pairs of language fragments. What has barely been voiced yet, though, is the idea to let go of one’s own modeling language, and use one from the other area instead. We advocate that idea here – to use automata-network languages from model checking instead of PDDL – motivated by modeling difficulties relating to planning agents surrounded by exogenous agents in complex environments. One could, of course, address this by designing additional extended planning languages. But one can also leverage decades of work on modeling in the formal methods community, creating potential for deep synergy and integration with their techniques as a side effect. We believe there’s a case to be made for the latter, as one modeling alternative in planning among others.
    @inproceedings{Hoffmann_AAAI2020,
        author = "Hoffmann, Joerg and Hermanns, Holger and Klauck, Michela and Steinmetz, Marcel and Karpas, Erez and Magazzeni, Daniele",
        title = "{Let's Learn their Language? A Case for Planning with Automata-Network Languages from Model Checking}",
        booktitle = "Proceedings of AAAI Conference on Artificial Intelligence (AAAI 2020)",
        year = "2020",
        publisher = "AAAI",
        address = "",
        pages = "",
        isbn = "",
        doi = ""
    }

  • Automated Planning for Robotics. Erez Karpas and Daniele Magazzeni; Annual Review of Control, Robotics, and Autonomous Systems, 2020.

    Abstract
    Abstract:
    Modern robots are increasingly capable of performing “basic” activities such as localization, navigation, and motion planning. However, for a robot to be considered intelligent, we would like it to be able to automatically combine these capabilities in order to achieve a high-level goal. The field of automated planning (sometimes called AI planning) deals with automatically synthesizing plans that combine basic actions to achieve a high-level goal. In this article, we focus on the intersection of automated planning and robotics and discuss some of the challenges and tools available to employ automated planning in controlling robots. We review different types of planning formalisms and discuss their advantages and limitations, especially in the context of planning robot actions. We conclude with a brief guide aimed at helping roboticists choose the right planning model to endow a robot with planning capabilities.
    @article{Karpas_2020,
        author = "Karpas, Erez and Magazzeni, Daniele",
        title = "{Automated Planning for Robotics}",
        journal = "Annual Review of Control, Robotics, and Autonomous Systems",
        year = "2020",
        doi = "10.1146/annurev-control-082619-100135"
    }

2019

  • An Introduction to the Planning Domain Definition Language. Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni and Christian Muise; Synthesis Lectures on Artificial Intelligence and Machine Learning, 2019.

    Abstract
    Abstract:
    Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input language of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems. The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.
    @book{PDDLbook2019,
        author = "Haslum, Patrik and Lipovetzky, Nir and Magazzeni, Daniele and Muise, Christian",
        title = "{An Introduction to the Planning Domain Definition Language}",
        booktitle = "Synthesis Lectures on Artificial Intelligence and Machine Learning",
        year = "2019",
        publisher = "Morgan \& Claypool Publishers 2019",
        address = "",
        pages = "",
        isbn = "",
        doi = "10.2200/S00900ED2V01Y201902AIM042"
    }

  • Towards Explainable Planning as a Service. Michael Cashmore, Anna Collins, Benjamin Krarup, Senka Krivic, Daniele Magazzeni and David Smith; ICAPS-19 Workshop on Explainable Planning, 2019.

    Abstract
    Abstract:
    Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service – that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Planning as a service that identify several possible research directions.
    @inproceedings{Cashmore_icapsxai2019,
        author = "Cashmore, Michael and Collins, Anna and Krarup, Benjamin and Krivic, Senka and Magazzeni, Daniele and Smith, David",
        title = "{Towards Explainable Planning as a Service}",
        booktitle = "ICAPS-19 Workshop on Explainable Planning",
        year = "2019"
    }

  • Model-Based Contrastive Explanations for Explainable Planning. Benjamin Krarup, Michael Cashmore, Daniele Magazzeni and Tim Miller; ICAPS-19 Workshop on Explainable Planning, 2019.

    Abstract
    Abstract:
    An important type of question that arises in Explainable Planning is a contrastive question, of the form “Why action A instead of action B?”. These kinds of questions can be answered with a contrastive explanation that compares properties of the original plan containing A against the contrastive plan containing B. An effective explanation of this type serves to highlight the differences between the decisions that have been made by the planner and what the user would expect, as well as to provide further insight into the model and the planning process. Producing this kind of explanation requires the generation of the contrastive plan. This paper introduces domain-independent compilations of user questions into constraints. These constraints are added to the planning model, so that a solution to the new model represents the contrastive plan. We introduce a formal description of the compilation from user question to constraints in a temporal and numeric PDDL2.1 planning setting.
    @inproceedings{Krarup_icaps2019,
        author = "Krarup, Benjamin and Cashmore, Michael and Magazzeni, Daniele and Miller, Tim",
        title = "{Model-Based Contrastive Explanations for Explainable Planning}",
        booktitle = "ICAPS-19 Workshop on Explainable Planning",
        year = "2019"
    }

  • Towards an Argumentation-based Approach to Explainable Planning. Anna Collins, Daniele Magazzeni and Simon Parsons; ICAPS-19 Workshop on Explainable Planning, 2019.

    Abstract
    Abstract:
    Providing transparency of AI planning systems is crucial for their success in practical applications. In order to create a transparent system, a user must be able to query it for explanations about its outputs. We argue that a key underlying principle for this is the use of causality within a planning model, and that argumentation frameworks provide an intuitive representation of such causality. In this paper, we discuss how argumentation can aid in extracting causalities in plans and models, and how they can create explanations from them.
    @inproceedings{Collins_icaps2019,
        author = "Collins, Anna and Magazzeni, Daniele and Parsons, Simon",
        title = "{Towards an Argumentation-based Approach to Explainable Planning}",
        booktitle = "ICAPS-19 Workshop on Explainable Planning",
        year = "2019"
    }

  • Explaining the Space of Plans through Plan-Property Dependencies. Rebecca Eifler, Michael Cashmore, Jörg Hoffmann, Daniele Magazzeni and Marcel Steinmetz; ICAPS-19 Workshop on Explainable Planning, 2019.

    Abstract
    Abstract:
    A key problem in explainable AI planning is to elucidate decision rationales. User questions in this context are often contrastive, taking the form “Why do A rather than B?”. Answering such a question requires a statement about the space of possible plans. We propose to do so through plan-property dependencies, where plan properties are Boolean properties of plans the user is interested in, and dependencies are entailment relations in plan space. The answer to the above question then consists of those properties C entailed by B. We introduce a formal framework for such dependency analysis. We instantiate and operationalize that framework for the case of dependencies between goals in oversubscription planning. More powerful plan properties can be compiled into that special case. We show experimentally that, in a variety of benchmarks, the suggested analyses can be feasible and produce compact answers for human inspection.
    @inproceedings{Eifler_icaps2019,
        author = {Eifler, Rebecca and Cashmore, Michael and Hoffmann, J\"org and Magazzeni, Daniele and Steinmetz, Marcel},
        title = "{Explaining the Space of Plans through Plan-Property Dependencies}",
        booktitle = "ICAPS-19 Workshop on Explainable Planning",
        year = "2019"
    }

  • Probabilistic Planning for Robotics with ROSPlan. Gerard Canal, Michael Cashmore, Senka Krivić, Guillem Alenyà, Daniele Magazzeni and Carme Torras; Towards Autonomous Robotic Systems, pp. 236-250, 2019.

    Abstract
    Abstract:
    Probabilistic planning is very useful for handling uncertainty in planning tasks to be carried out by robots. ROSPlan is a framework for task planning in the Robot Operating System (ROS), but until now it has not been possible to use probabilistic planners within the framework. This systems paper presents a standardized integration of probabilistic planners into ROSPlan that allows for reasoning with non-deterministic effects and is agnostic to the probabilistic planner used. We instantiate the framework in a system for the case of a mobile robot performing tasks indoors, where probabilistic plans are generated and executed by the PROST planner. We evaluate the effectiveness of the proposed approach in a real-world robotic scenario.
    @inproceedings{Canal_taros2019,
        author = "Canal, Gerard and Cashmore, Michael and Krivi\'{c}, Senka and Alenyà, Guillem and Magazzeni, Daniele and Torras, Carme",
        title = "{Probabilistic Planning for Robotics with ROSPlan}",
        booktitle = "Towards Autonomous Robotic Systems",
        year = "2019",
        publisher = "Springer International Publishing",
        address = "Cham",
        pages = "236--250",
        isbn = "978-3-030-23807-0",
        doi = "10.1007/978-3-030-23807-0\_20"
    }

  • Robustness Envelopes for Temporal Plans. Michael Cashmore, Alessandro Cimatti, Andrea Micheli, Daniele Magazzeni and Parisa Zehtabi; Proceedings of AAAI Conference on Artificial Intelligence (AAAI 2019), 2019.

    Abstract
    Abstract:
    To achieve practical execution, planners must produce temporal plans with some degree of run-time adaptability. Such plans can be expressed as Simple Temporal Networks (STN), that constrain the timing of action activations, and implicitly represent the space of choices for the plan executor. A first problem is to verify that all the executor choices allowed by the STN plan will be successful, i.e. the plan is valid. An even more important problem is to assess the effect of discrepancies between the model used for planning and the execution environment. We propose an approach to compute the “robustness envelope” (i.e., alternative action durations or resource consumption rates) of a given STN plan, for which the plan remains valid. Plans can have boolean and numeric variables as well as discrete and continuous change. We leverage Satisfiability Modulo Theories (SMT) to make the approach formal and practical.
    @inproceedings{Cashmore_aaai2019,
        author = "Cashmore, Michael and Cimatti, Alessandro and Micheli, Andrea and Magazzeni, Daniele and Zehtabi, Parisa",
        title = "{Robustness Envelopes for Temporal Plans}",
        booktitle = "Proceedings of AAAI Conference on Artificial Intelligence (AAAI 2019)",
        year = "2019"
    }

  • Replanning for Situated Robots. Michael Cashmore, Andrew Coles, Bence Cserna, Erez Karpas, Daniele Magazzeni and Wheeler Ruml; Proceedings of International Conference on Automated Planning and Scheduling (ICAPS 2019), 2019.

    Abstract
    Abstract:
    Planning enables intelligent agents, such as robots, to act so as to achieve their long term goals. To make the planning process tractable, a relatively low fidelity model of the world is often used, which sometimes leads to the need to replan. The typical view of replanning is that the robot is given the current state, the goal, and possibly some data from the previous planning process. However, for robots (or teams of robots) that exist in continuous physical space, act concurrently, have deadlines, or must otherwise consider durative actions, things are not so simple. In this paper, we address the problem of replanning for situated robots. Relying on previous work on situated temporal planning, we frame the replanning problem as a situated temporal planning problem, where currently executing actions are handled via Timed Initial Literals (TILs), under the assumption that actions cannot be interrupted. We then relax this assumption, and address situated replanning with interruptible actions. We bridge the gap between the low-level model of the robot and the high-level model used for planning by the novel notion of a bail out action generator, which relies on the low-level model to generate highlevel actions that describe possible ways to interrupt currently executing actions. Because actions can be interrupted at different times during their execution, we also propose a novel algorithm to handle temporal planning with time-dependent durations.
    @inproceedings{Cashmore_icaps2019,
        author = "Cashmore, Michael and Coles, Andrew and Cserna, Bence and Karpas, Erez and Magazzeni, Daniele and Ruml, Wheeler",
        title = "{Replanning for Situated Robots}",
        booktitle = "Proceedings of International Conference on Automated Planning and Scheduling (ICAPS 2019)",
        year = "2019"
    }

  • Temporal Planning as Refinement-Based Model Checking. Alex Heinz, Martin Wehrle, Sergiy Bogomolov, Daniele Magazzeni, Marius Greitschus and Andreas Podelski; Proceedings of International Conference on Automated Planning and Scheduling (ICAPS 2019), 2019.

    @inproceedings{Heinz_icaps2019,
        author = "Heinz, Alex and Wehrle, Martin and Bogomolov, Sergiy and Magazzeni, Daniele and Greitschus, Marius and Podelski, Andreas",
        title = "{Temporal Planning as Refinement-Based Model Checking}",
        booktitle = "Proceedings of International Conference on Automated Planning and Scheduling (ICAPS 2019)",
        year = "2019"
    }

2018

  • Integrating Temporal Reasoning and Sampling-Based Motion Planning for Multigoal Problems With Dynamics and Time Windows. Stefan Edelkamp, Morteza Lahijanian, Daniele Magazzeni and Erion Plaku; IEEE Robotics and Automation Letters, 3:3473-3480, 2018.

    Abstract
    Abstract:
    Robots used for inspection, package deliveries, moving of goods, and other logistics operations are often required to visit certain locations within specified time bounds. This gives rise to a challenging problem as it requires not only planning collisionfree and dynamically feasible motions but also reasoning temporally about when and where the robot should be. While significant progress has been made in integrating task and motion planning, there are still no effective approaches for multigoal motion planning when both dynamics and time windows must be satisfied. To effectively solve this challenging problem, this paper develops an approach that couples temporal planning over a discrete abstraction with sampling-based motion planning over the continuous state space of feasible motions. The discrete abstraction is obtained by imposing a roadmap that captures the connectivity of the free space. At each iteration of a core loop, the approach first invokes the temporal planner to find a solution over the roadmap abstraction. In a second step, the approach uses sampling to expand a motion tree along the regions associated with the discrete solution. Experiments are conducted with second-order ground and aerial vehicle models operating in complex environments. Results demonstrate the efficiency and scalability of the approach as we increase the number of goals and the difficulty of satisfying the time bounds.
    @article{Edelkamp_ieee2018,
        author = "Edelkamp, Stefan and Lahijanian, Morteza and Magazzeni, Daniele and Plaku, Erion",
        title = "{Integrating Temporal Reasoning and Sampling-Based Motion Planning for Multigoal Problems With Dynamics and Time Windows}",
        journal = "{IEEE} Robotics and Automation Letters",
        volume = "3",
        number = "4",
        pages = "3473--3480",
        year = "2018",
        url = "https://doi.org/10.1109/LRA.2018.2853642",
        doi = "10.1109/LRA.2018.2853642"
    }

  • Towards Providing Explanations for AI Planner Decisions. Rita Borgo, Michael Cashmore and Daniele Magazzeni; CoRR, abs/1810.06338, 2018.

    Abstract
    Abstract:
    In order to engender trust in AI, humans must understand what an AI system is trying to achieve, and why. To overcome this problem, the underlying AI process must produce justifications and explanations that are both transparent and comprehensible to the user. AI Planning is well placed to be able to address this challenge. In this paper we present a methodology to provide initial explanations for the decisions made by the planner. Explanations are created by allowing the user to suggest alternative actions in plans and then compare the resulting plans with the one found by the planner. The methodology is implemented in the new XAI-PLAN framework.
    @article{Borgo_corr2018,
        author = "Borgo, Rita and Cashmore, Michael and Magazzeni, Daniele",
        title = "{Towards Providing Explanations for {AI} Planner Decisions}",
        journal = "CoRR",
        volume = "abs/1810.06338",
        year = "2018",
        url = "http://arxiv.org/abs/1810.06338"
    }

  • Explainable Security. Luca Viganò and Daniele Magazzeni; CoRR, abs/1807.04178, 2018.

    Abstract
    Abstract:
    The Defense Advanced Research Projects Agency (DARPA) recently launched the Explainable Artificial Intelligence (XAI) program that aims to create a suite of new AI techniques that enable end users to understand, appropriately trust, and effectively manage the emerging generation of AI systems. In this paper, inspired by DARPA’s XAI program, we propose a new paradigm in security research: Explainable Security (XSec). We discuss the “Six Ws” of XSec (Who? What? Where? When? Why? and How?) and argue that XSec has unique and complex characteristics: XSec involves several different stakeholders (i.e., the system’s developers, analysts, users and attackers) and is multi-faceted by nature (as it requires reasoning about system model, threat model and properties of security, privacy and trust as well as about concrete attacks, vulnerabilities and countermeasures). We define a roadmap for XSec that identifies several possible research directions.
    @article{Vigano_corr2018,
        author = "Vigan{\`{o}}, Luca and Magazzeni, Daniele",
        title = "{Explainable Security}",
        journal = "CoRR",
        volume = "abs/1807.04178",
        year = "2018",
        url = "http://arxiv.org/abs/1807.04178"
    }

  • Temporal Planning while the Clock Ticks. Michael Cashmore, Andrew Coles, Bence Cserna, Erez Karpas, Daniele Magazzeni and Wheeler Ruml; Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling, ICAPS 2018, pp. 39-46, 2018.

    Abstract
    Abstract:
    One of the original motivations for domain-independent planning was to generate plans that would then be executed in the environment. However, most existing planners ignore the passage of time during planning. While this can work well when absolute time does not play a role, this approach can lead to plans failing when there are external timing constraints, such as deadlines. In this paper, we describe a new approach for time-sensitive temporal planning. Our planner is aware of the fact that plan execution will start only once planning finishes, and incorporates this information into its decision making, in order to focus the search on branches that are more likely to lead to plans that will be feasible when the planner finishes.
    @inproceedings{Cashmore_icaps2018,
        author = "Cashmore, Michael and Coles, Andrew and Cserna, Bence and Karpas, Erez and Magazzeni, Daniele and Ruml, Wheeler",
        title = "{Temporal Planning while the Clock Ticks}",
        booktitle = "Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling, {ICAPS} 2018",
        pages = "39--46",
        year = "2018",
        url = "https://aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/view/17724"
    }

  • Planning and Operations Research (Dagstuhl Seminar 18071). J. Christopher Beck, Daniele Magazzeni, Gabriele Röger and Willem-Jan van Hoeve; Dagstuhl Reports, 8:26-63, 2018.

    Abstract
    Abstract:
    This report documents the program and the outcomes of Dagstuhl Seminar 18071 “Planning and Operations Research”. The seminar brought together researchers in the areas of Artificial Intelligence (AI) Planning, Constraint Programming, and Operations Research. All three areas have in common that they deal with complex systems where a huge space of interacting options makes it almost impossible to humans to take optimal or even good decisions. From a historical perspective, operations research stems from the application of mathematical methods to (mostly) industrial applications while planning and constraint programming emerged as subfields of artificial intelligence where the emphasis was traditionally more on symbolic and logical search techniques for the intelligent selection and sequencing of actions to achieve a set of goals. Therefore operations research often focuses on the allocation of scarce resources such as transportation capacity, machine availability, production materials, or money, while planning focuses on the right choice of actions from a large space of possibilities. While this difference results in problems in different complexity classes, it is often possible to cast the same problem as an OR, CP, or planning problem. In this seminar, we investigated the commonalities and the overlap between the different areas to learn from each other’s expertise, bring the communities closer together, and transfer knowledge about solution techniques that can be applied in all areas.
    @article{Magazzeni_dagstuhlreports2019,
        author = {Beck, J. Christopher and Magazzeni, Daniele and R{\"{o}}ger, Gabriele and van Hoeve, Willem{-}Jan},
        title = "{Planning and Operations Research (Dagstuhl Seminar 18071)}",
        journal = "Dagstuhl Reports",
        volume = "8",
        number = "2",
        pages = "26--63",
        year = "2018",
        url = "https://doi.org/10.4230/DagRep.8.2.26",
        doi = "10.4230/DagRep.8.2.26"
    }

  • Opportunistic Planning in Autonomous Underwater Missions. Michael Cashmore, Maria Fox, Derek Long, Daniele Magazzeni and Bram Ridder; IEEE Trans. Automation Science and Engineering, 15:519-530, 2018.

    Abstract
    Abstract:
    This paper explores the execution of planned autonomous underwater vehicle (AUV) missions where opportunities to achieve additional utility can arise during execution. The missions are represented as temporal planning problems, with hard goals and time constraints. Opportunities are soft goals with high utility. The probability distributions for the occurrences of these opportunities are not known, but it is known that they are unlikely, so it is not worth trying to anticipate their occurrence prior to plan execution. However, as they are high utility, it is worth trying to address them dynamically when they are encountered, as long as this can be done without sacrificing the achievement of the hard goals of the problem. We formally characterize the opportunistic planning problem, introduce a novel approach to opportunistic planning, and compare it with an on-board replanning approach in the domain of AUVs performing pillar expection and chain-following tasks.
    @article{Cashmore_ieee2018,
        author = "Cashmore, Michael and Fox, Maria and Long, Derek and Magazzeni, Daniele and Ridder, Bram",
        title = "{Opportunistic Planning in Autonomous Underwater Missions}",
        journal = "{IEEE} Trans. Automation Science and Engineering",
        volume = "15",
        number = "2",
        pages = "519--530",
        year = "2018",
        url = "https://doi.org/10.1109/TASE.2016.2636662",
        doi = "10.1109/TASE.2016.2636662"
    }

  • Explaining Rebel Behavior in Goal Reasoning Agents. Dustin Dannenhauer, Michael Floyd, Daniele Magazzeni and David Aha; Proceedings of ICAPS-18 Workshop on Explainable Planning, 2018.

    Abstract
    Abstract:
    Generating human-comprehensible explanations is an important requirement for autonomous systems in human-agent teaming environments. Humans and agents often have their own knowledge of the world, knowledge of objectives being pursued and tasks being performed, and their own constraints. Given these differences, an agent may be issued goals that violate its own constraints or preferences, or are undesirable for the team’s task. Numerous situations may arise where rebellion by dropping or changing goals leads to a more beneficial outcome. Agents with goal reasoning capabilities may rebel by rejecting or altering the goals and plans expected of them by human teammates. Explanations help build trust and understanding between the human and agent, leading to greater overall effectiveness. In this paper we outline motivating examples for explainable rebellious behavior in goal reasoning systems and identify open research questions.
    @inproceedings{Dannenhauer_icaps2019,
        author = "Dannenhauer, Dustin and Floyd, Michael and Magazzeni, Daniele and Aha, David",
        title = "{Explaining Rebel Behavior in Goal Reasoning Agents}",
        booktitle = "Proceedings of ICAPS-18 Workshop on Explainable Planning",
        year = "2018"
    }

  • Strategic-Tactical Planning for Autonomous Underwater Vehicles over Long Horizons. Dorian Buksz, Michael Cashmore, Benjamin Krarup, Daniele Magazzeni and Bram Ridder; 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, Madrid, Spain, October 1-5, 2018, pp. 3565-3572, 2018.

    Abstract
    Abstract:
    In challenging environments where human intervention is expensive, robust and persistent autonomy is a key requirement. AI Planners can efficiently construct plans to achieve this long-term autonomous behaviour. However, in plans which are expected to last over days, or even weeks, the size of the state-space becomes too large for current planners to solve as a single problem. These problems are well-suited to decomposition and abstraction planning techniques. We present a novel approach in the context of persistent autonomy in autonomous underwater vehicles, in which tasks are complex and diverse and plans cannot be precomputed. Our approach performs a decomposition into a two-level hierarchical structure, which dynamically constructs planning problems at the upper level of the hierarchy using solution plans from the lower level. Solution plans are then executed and monitored simultaneously at both levels. We evaluate the approach, showing that compared to strictly top-down hierarchical decompositions, our approach leads to more robust solution plans of higher quality.
    @inproceedings{Buksz_iros2018,
        author = "Buksz, Dorian and Cashmore, Michael and Krarup, Benjamin and Magazzeni, Daniele and Ridder, Bram",
        title = "{Strategic-Tactical Planning for Autonomous Underwater Vehicles over Long Horizons}",
        booktitle = "2018 {IEEE/RSJ} International Conference on Intelligent Robots and Systems, {IROS} 2018, Madrid, Spain, October 1-5, 2018",
        pages = "3565--3572",
        year = "2018",
        url = "https://doi.org/10.1109/IROS.2018.8594347",
        doi = "10.1109/IROS.2018.8594347"
    }

  • Situated Planning for Execution Under Temporal Constraints. Michael Cashmore, Andrew Coles, Bence Cserna, Erez Karpas, Daniele Magazzeni and Wheeler Ruml; 2018 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 26-28, 2018., 2018.

    Abstract
    Abstract:
    One of the original motivations for domain-independent planning was to generate plans that would then be executed in the environment. However, most existing planners ignore the passage of time during planning. While this can work well when absolute time does not play a role, this approach can lead to plans failing when there are external timing constraints, such as deadlines. In this paper, we describe a new approach for time-sensitive temporal planning. Our planner is aware of the fact that plan execution will start only once planning finishes, and incorporates this information into its decision making, in order to focus the search on branches that are more likely to lead to plans that will be feasible when the planner finishes.
    @inproceedings{Cashmore_aaai2018,
        author = "Cashmore, Michael and Coles, Andrew and Cserna, Bence and Karpas, Erez and Magazzeni, Daniele and Ruml, Wheeler",
        title = "{Situated Planning for Execution Under Temporal Constraints}",
        booktitle = "2018 {AAAI} Spring Symposia, Stanford University, Palo Alto, California, USA, March 26-28, 2018.",
        year = "2018",
        url = "https://aaai.org/ocs/index.php/SSS/SSS18/paper/view/17452"
    }

  • User Interfaces and Scheduling and Planning: Workshop Summary and Proposed Challenges. Richard G. Freedman, Tathagata Chakraborti, Kartik Talamadupula, Daniele Magazzeni and Jeremy D. Frank; 2018 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 26-28, 2018., 2018.

    Abstract
    Abstract:
    The User Interfaces and Scheduling and Planning (UISP) Workshop had its inaugural meeting at the 2017 International Conference on Automated Scheduling and Planning(ICAPS). The UISP community focuses on bridging the gap between automated planning and scheduling technologies and user interface (UI) technologies. Planning and scheduling systems need UIs, and UIs can be designed and built using planning and scheduling systems. The workshop participants included representatives from government organizations, industry, and academia with various insights and novel challenges. We summarize the discussions from the workshop as well as outline challenges related to this area of research, introducing the now formally established field to the broader user experience and artificial intelligence communities.
    @inproceedings{Freedman_aaai2018,
        author = "Freedman, Richard G. and Chakraborti, Tathagata and Talamadupula, Kartik and Magazzeni, Daniele and Frank, Jeremy D.",
        title = "{User Interfaces and Scheduling and Planning: Workshop Summary and Proposed Challenges}",
        booktitle = "2018 {AAAI} Spring Symposia, Stanford University, Palo Alto, California, USA, March 26-28, 2018.",
        year = "2018",
        url = "https://aaai.org/ocs/index.php/SSS/SSS18/paper/view/17520"
    }

2017

  • PDDL+ Planning with Temporal Pattern Databases. Wiktor Mateusz Piotrowski, Maria Fox, Derek Long, Daniele Magazzeni and Fabio Mercorio; The Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, Saturday, February 4-9, 2017, San Francisco, California, USA, 2017.

    Abstract
    Abstract:
    The introduction of PDDL+ allowed more accurate representations of complex real-world problems of interest to the scientific community. However, PDDL+ problems are notoriously challenging to planners, requiring more advanced heuristics. We introduce the Temporal Pattern Database (TPDB), a new domain-independent heuristic technique designed for PDDL+ domains with mixed discrete/continuous behaviour, non-linear system dynamics, processes, and events. The pattern in the TPDB is obtained through an abstraction based on time and state discretisation. Our approach combines constraint relaxation and abstraction techniques, and uses solutions to the relaxed problem, as a guide to solving the concrete problem with a discretisation fine enough to satisfy the continuous model’s constraints.
    @inproceedings{Piotrowski_aaai2019,
        author = "Piotrowski, Wiktor Mateusz and Fox, Maria and Long, Derek and Magazzeni, Daniele and Mercorio, Fabio",
        title = "{{PDDL+} Planning with Temporal Pattern Databases}",
        booktitle = "The Workshops of the The Thirty-First {AAAI} Conference on Artificial Intelligence, Saturday, February 4-9, 2017, San Francisco, California, {USA}",
        year = "2017",
        url = "http://aaai.org/ocs/index.php/WS/AAAIW17/paper/view/15193"
    }

  • Landmarks for Numeric Planning Problems. Enrico Scala, Patrik Haslum, Daniele Magazzeni and Sylvie Thiébaux; Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, pp. 4384-4390, 2017.

    Abstract
    Abstract:
    The paper generalises the notion of landmarks for reasoning about planning problems involving propositional and numeric variables. Intuitively, numeric landmarks are regions in the metric space defined by the problem whose crossing is necessary for its resolution. The paper proposes a relaxation-based method for their automated extraction directly from the problem structure, and shows how to exploit them to infer what we call disjunctive and additive hybrid action landmarks. The justification of such a disjunctive representation results from the intertwined propositional and numeric structure of the problem. The paper exercises their use in two novel admissible LP-Based numeric heuristics, and reports experiments on cost-optimal numeric planning problems. Results show the heuristics are more informed and effective than previous work for problems involving a higher number of (sub)goals.
    @inproceedings{Scala_ijcai2017,
        author = "Scala, Enrico and Haslum, Patrik and Magazzeni, Daniele and Thi{\'{e}}baux, Sylvie",
        title = "{Landmarks for Numeric Planning Problems}",
        booktitle = "Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, {IJCAI} 2017, Melbourne, Australia, August 19-25, 2017",
        pages = "4384--4390",
        year = "2017",
        url = "https://doi.org/10.24963/ijcai.2017/612",
        doi = "10.24963/ijcai.2017/612"
    }

  • Validation and Verification of Smart Contracts: A Research Agenda. Daniele Magazzeni, Peter McBurney and William Nash; IEEE Computer, 50:50-57, 2017.

    Abstract
    Abstract:
    Smart contracts might encode legal contracts written in natural language to represent the contracting parties’ shared understandings and intentions. The issues and research challenges involved in the validation and verification of smart contracts, particularly those running over blockchains and distributed ledgers, are explored.
    @article{Magazzeni_ieee_2017,
        author = "Magazzeni, Daniele and McBurney, Peter and Nash, William",
        title = "{Validation and Verification of Smart Contracts: {A} Research Agenda}",
        journal = "{IEEE} Computer",
        volume = "50",
        number = "9",
        pages = "50--57",
        year = "2017",
        url = "https://doi.org/10.1109/MC.2017.3571045",
        doi = "10.1109/MC.2017.3571045"
    }

  • CASP Solutions for Planning in Hybrid Domains. Marcello Balduccini, Daniele Magazzeni, Marco Maratea and Emily Leblanc; CoRR, abs/1704.03574, 2017.

    Abstract
    Abstract:
    CASP is an extension of ASP that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics. In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP CASP solver in order to solve CASP programs arising from PDDL+ domains. An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of the EZCSP solver, other CASP solvers, and PDDL+ planners, shows the viability of our solution.
    @article{Balduccini_corr2017,
        author = "Balduccini, Marcello and Magazzeni, Daniele and Maratea, Marco and Leblanc, Emily",
        title = "{{CASP} Solutions for Planning in Hybrid Domains}",
        journal = "CoRR",
        volume = "abs/1704.03574",
        year = "2017",
        url = "http://arxiv.org/abs/1704.03574"
    }

  • Explainable Planning. Maria Fox, Derek Long and Daniele Magazzeni; CoRR, abs/1709.10256, 2017.

    Abstract
    Abstract:
    As AI is increasingly being adopted into application solutions, the challenge of supporting interaction with humans is becoming more apparent. Partly this is to support integrated working styles, in which humans and intelligent systems cooperate in problem-solving, but also it is a necessary step in the process of building trust as humans migrate greater responsibility to such systems. The challenge is to find effective ways to communicate the foundations of AI-driven behaviour, when the algorithms that drive it are far from transparent to humans. In this paper we consider the opportunities that arise in AI planning, exploiting the model-based representations that form a familiar and common basis for communication with users, while acknowledging the gap between planning algorithms and human problem-solving.
    @article{Fox_corr2017,
        author = "Fox, Maria and Long, Derek and Magazzeni, Daniele",
        title = "{Explainable Planning}",
        journal = "CoRR",
        volume = "abs/1709.10256",
        year = "2017",
        url = "http://arxiv.org/abs/1709.10256"
    }

  • Decreasing Uncertainty in Planning with State Prediction. Senka Krivić, Michael Cashmore, Daniele Magazzeni, Bram Ridder, Sándor Szedmák and Justus H. Piater; Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, pp. 2032-2038, 2017.

    Abstract
    Abstract:
    In real world environments the state is almost never completely known. Exploration is often expensive. The application of planning in these environments is consequently more difficult and less robust. In this paper we present an approach for predicting new information about a partially-known state. The state is translated into a partially-known multigraph, which can then be extended using machine-learning techniques. We demonstrate the effectiveness of our approach, showing that it enhances the scalability of our planners, and leads to less time spent on sensing actions.
    @inproceedings{Krivic_ijcai2017,
        author = "Krivi\'c, Senka and Cashmore, Michael and Magazzeni, Daniele and Ridder, Bram and Szedm{\'{a}}k, S{\'{a}}ndor and Piater, Justus H.",
        title = "{Decreasing Uncertainty in Planning with State Prediction}",
        booktitle = "Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, {IJCAI} 2017, Melbourne, Australia, August 19-25, 2017",
        pages = "2032--2038",
        year = "2017",
        url = "https://doi.org/10.24963/ijcai.2017/282",
        doi = "10.24963/ijcai.2017/282"
    }

  • CASP solutions for planning in hybrid domains. Marcello Balduccini, Daniele Magazzeni, Marco Maratea and Emily Leblanc; TPLP, 17:591-633, 2017.

    Abstract
    Abstract:
    Constraint answer set programming (CASP) is an extension of answer set programming that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics. In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the ezcsp CASP solver in order to solve CASP programs arising from PDDL+ domains. An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of the ezcsp solver, other CASP solvers, and PDDL+ planners, shows the viability of our solution.
    @article{Balduccini_tplp_2017,
        author = "Balduccini, Marcello and Magazzeni, Daniele and Maratea, Marco and Leblanc, Emily",
        title = "{{CASP} solutions for planning in hybrid domains}",
        journal = "{TPLP}",
        volume = "17",
        number = "4",
        pages = "591--633",
        year = "2017",
        url = "https://doi.org/10.1017/S1471068417000187",
        doi = "10.1017/S1471068417000187"
    }

  • Short-Term Human-Robot Interaction through Conditional Planning and Execution. Valerio Sanelli, Michael Cashmore, Daniele Magazzeni and Luca Iocchi; Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling, ICAPS 2017, Pittsburgh, Pennsylvania, USA, June 18-23, 2017., pp. 540-548, 2017.

    Abstract
    Abstract:
    The deployment of robots in public environments is gaining more and more attention and interest both for the research opportunities and for the possibility of developing commercial applications over it. In these scenarios, proper definitions and implementations of human-robot interactions are crucial and the specific characteristics of the environment (in particular, the presence of untrained users) makes the task of defining and implementing effective interactions particularly challenging. In this paper, we describe a method and a fully implemented robotic system using conditional planning for generating and executing short-term interactions by a robot deployed in a public environment. To this end, the proposed method integrates and extends two components already successfully used for planning in robotics: ROSPlan and Petri Net Plans. The contributions of this paper are the problem definition of generating short-term interactions as a conditional planning problem and the description of a solution fully implemented on a real robot. The proposed method is based on the integration between a contingent planner in ROSPlan and the Petri Net Plans execution framework, and it has been tested in different scenarios where the robot interacted with hundreds of untrained users.
    @inproceedings{Sanelli_icaps2017,
        author = "Sanelli, Valerio and Cashmore, Michael and Magazzeni, Daniele and Iocchi, Luca",
        title = "{Short-Term Human-Robot Interaction through Conditional Planning and Execution}",
        booktitle = "Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling, {ICAPS} 2017, Pittsburgh, Pennsylvania, USA, June 18-23, 2017.",
        pages = "540--548",
        year = "2017",
        url = "https://aaai.org/ocs/index.php/ICAPS/ICAPS17/paper/view/15750"
    }

  • Initial State Prediction in Planning. Senka Krivić, Michael Cashmore, Bram Ridder, Daniele Magazzeni, Sándor Szedmák and Justus H. Piater; The Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, Saturday, February 4-9, 2017, San Francisco, California, USA, 2017.

    Abstract
    Abstract:
    While recent advances in offline reasoning techniques and online execution strategies have made planning under uncertainty more robust, the application of plans in partially-known environments is still a difficult and important topic. In this paper we present an approach for predicting new information about a partially-known initial state, represented as a multigraph utilizing Maximum-Margin Multi-Valued Regression. We evaluate this approach in four different domains, demonstrating high recall and accuracy.
    @inproceedings{Krivic_aaai2017,
        author = "Krivi\'c, Senka and Cashmore, Michael and Ridder, Bram and Magazzeni, Daniele and Szedm{\'{a}}k, S{\'{a}}ndor and Piater, Justus H.",
        title = "{Initial State Prediction in Planning}",
        booktitle = "The Workshops of the The Thirty-First {AAAI} Conference on Artificial Intelligence, Saturday, February 4-9, 2017, San Francisco, California, {USA}",
        year = "2017",
        url = "http://aaai.org/ocs/index.php/WS/AAAIW17/paper/view/15157"
    }

  • On-the-fly detection of novel objects in indoor environments. Edith Langer, Bram Ridder, Michael Cashmore, Daniele Magazzeni, Michael Zillich and Markus Vincze; 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017, Macau, China, December 5-8, 2017, pp. 900-907, 2017.

    Abstract
    Abstract:
    Many robotic applications require the detection of new objects in known environments. Common approaches navigate in the environment using pre-defined waypoints and segment the scene at these waypoints. Without knowing where to find new objects, this process can be time-consuming and prone to detecting false positives. To overcome these limitations we propose an approach that combines navigation and attention in order to detect novel objects rapidly. We exploit the octomap, created by the robot while it navigates in the environment, as a pre-attention filter to suggest potential regions of interest. These regions are then visited to obtain a close-up view for better object detection and recognition. We evaluate our approach in a simulated as well as a real environment. The experiments show that our approach outperforms previous approaches in terms of runtime and the number of segmentation actions required to find all novel objects in the environment.
    @inproceedings{Langer_ieee2017,
        author = "Langer, Edith and Ridder, Bram and Cashmore, Michael and Magazzeni, Daniele and Zillich, Michael and Vincze, Markus",
        title = "{On-the-fly detection of novel objects in indoor environments}",
        booktitle = "2017 {IEEE} International Conference on Robotics and Biomimetics, {ROBIO} 2017, Macau, China, December 5-8, 2017",
        pages = "900--907",
        year = "2017",
        url = "https://doi.org/10.1109/ROBIO.2017.8324532",
        doi = "10.1109/ROBIO.2017.8324532"
    }

  • Planning and Robotics (Dagstuhl Seminar 17031). Malik Ghallab, Nick Hawes, Daniele Magazzeni, Brian C. Williams and Andrea Orlandini; Dagstuhl Reports, 7:32-73, 2017.

    Abstract
    Abstract:
    This report documents the program and the outcomes of Dagstuhl Seminar 17031 on “Planning and Robotics”. The seminar was concerned with the synergy between the research areas of Automated Planning & Scheduling and Robotics. The motivation for this seminar was to bring together researchers from the two communities and people from the Industry in order to foster a broader interest in the integration of planning and deliberation approaches to sensory-motor functions in robotics. The first part of the seminar was dedicated to eight sessions composed on several topics in which attendees had the opportunity to present position statements. Then, the second part was composed by six panel sessions where attendees had the opportunity to further discuss the position statements and issues raised in previous sessions. The main outcomes were a greater common understanding of planning and robotics issues and challenges, and a greater appreciation of crossover between different perspectives, i.e., spanning from low level control to high-level cognitive approaches for autonomous robots. Different application domains were also discussed in which the deployment of planning and robotics methodologies and technologies constitute an added value.
    @article{Ghallab_dagstuhlreports2017,
        author = "Ghallab, Malik and Hawes, Nick and Magazzeni, Daniele and Williams, Brian C. and Orlandini, Andrea",
        title = "{Planning and Robotics (Dagstuhl Seminar 17031)}",
        journal = "Dagstuhl Reports",
        volume = "7",
        number = "1",
        pages = "32--73",
        year = "2017",
        url = "https://doi.org/10.4230/DagRep.7.1.32",
        doi = "10.4230/DagRep.7.1.32"
    }

2016

  • Planning Using Actions with Control Parameters. Emre Savas, Maria Fox, Derek Long and Daniele Magazzeni; ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands - Including Prestigious Applications of Artificial Intelligence (PAIS 2016), pp. 1185-1193, 2016.

    Abstract
    Abstract:
    Although PDDL is an expressive modelling language, a significant limitation is imposed on the structure of actions: the parameters of actions are restricted to values from finite (in fact, explicitly enumerated) domains. There is one exception to this, introduced in PDDL2.1, which is that durative actions may have durations that are chosen (possibly subject to explicit constraints in the action models) by the planner. A motivation for this limitation is that it ensures that the set of grounded actions is finite and, ignoring duration, the branching factor of action choices at a state is therefore finite. Although the duration parameter can make this choice infinite, very few planners support this possibility, but restrict themselves to durative actions with fixed durations. In this paper we motivate a proposed extension to PDDL to allow actions with infinite domain parameters, which we call control parameters. We illustrate reasons for using this modelling feature and then describe a planning approach that can handle domains that exploit it, implemented in a new planner, POPCORN (Partial-Order Planning with Constrained Real Numerics). We show that this approach scales to solve interesting problems.
    @inproceedings{Savas_ecai2016,
        author = "Savas, Emre and Fox, Maria and Long, Derek and Magazzeni, Daniele",
        title = "{Planning Using Actions with Control Parameters}",
        booktitle = "{ECAI} 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands - Including Prestigious Applications of Artificial Intelligence {(PAIS} 2016)",
        pages = "1185--1193",
        year = "2016",
        url = "https://doi.org/10.3233/978-1-61499-672-9-1185",
        doi = "10.3233/978-1-61499-672-9-1185"
    }

  • Heuristic Planning for PDDL+ Domains. Wiktor Mateusz Piotrowski, Maria Fox, Derek Long, Daniele Magazzeni and Fabio Mercorio; Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pp. 3213-3219, 2016.

    Abstract
    Abstract:
    Planning with hybrid domains modelled in PDDL+ has been gaining research interest in the Automated Planning community in recent years. Hybrid domain models capture a more accurate representation of real world problems that involve continuous processes than is possible using discrete systems. However, solving problems represented as PDDL+ domains is very challenging due to the construction of complex system dynamics, including non-linear processes and events. In this paper we introduce DiNo, a new planner capable of tackling complex problems with non-linear system dynamcs governing the continuous evolution of states. DiNo is based on the discretise-and-validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic, which is introduced in this paper. Although several planners have been developed to work with subsets of PDDL+ features, or restricted forms of processes, DiNo is currently the only heuristic planner capable of handling non-linear system dynamics combined with the full PDDL+ feature set.
    @inproceedings{Piotrowski_ijcai2016,
        author = "Piotrowski, Wiktor Mateusz and Fox, Maria and Long, Derek and Magazzeni, Daniele and Mercorio, Fabio",
        title = "{Heuristic Planning for {PDDL+} Domains}",
        booktitle = "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, {IJCAI} 2016, New York, NY, USA, 9-15 July 2016",
        pages = "3213--3219",
        year = "2016",
        url = "http://www.ijcai.org/Abstract/16/455"
    }

  • A Compilation of the Full PDDL+ Language into SMT. Michael Cashmore, Maria Fox, Derek Long and Daniele Magazzeni; Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling, ICAPS 2016, London, UK, June 12-17, 2016., pp. 79-87, 2016.

    Abstract
    Abstract:
    Planning in hybrid systems is important for dealing with real-world applications. PDDL+ supports this representation of domains with mixed discrete and continuous dynamics, and supports events and processes modelling exogenous change. Motivated by numerous SAT-based planning approaches, we propose an approach to PDDL+ planning through SMT, describing an SMT encoding that captures all the features of the PDDL+ problem as published by Fox and Long. The encoding can be applied on domains with nonlinear continuous change. We apply this encoding in a simple planning algorithm, demonstrating excellent results on a set of benchmark problems.
    @inproceedings{Cashmore_icaps2016,
        author = "Cashmore, Michael and Fox, Maria and Long, Derek and Magazzeni, Daniele",
        title = "{A Compilation of the Full {PDDL+} Language into {SMT}}",
        booktitle = "Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling, {ICAPS} 2016, London, UK, June 12-17, 2016.",
        pages = "79--87",
        year = "2016",
        url = "http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13101"
    }

  • Solving Realistic Unit Commitment Problems Using Temporal Planning: Challenges and Solutions. Chiara Piacentini, Daniele Magazzeni, Derek Long, Maria Fox and Chris Dent; Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling, ICAPS 2016, London, UK, June 12-17, 2016., pp. 421-430, 2016.

    Abstract
    Abstract:
    When facing real world planning problems, standard planners are often inadequate and enhancement of the current techniques are required. In this paper we present the challenges that we have faced in solving the Unit Commitment (UC) problem, a well-known problem in the electrical power industry for which current best methods are based on Mixed Integer Programming (MIP). Typical UC instances involve hundreds or even thousands of generating units, pushing the scalability of state of the art planners beyond their limits. Furthermore, UC is characterised by state-dependent action costs, a feature that not many domain independent planners can efficiently handle. In this paper we focus on the challenge of making domain-independent planning competitive with the MIP method on realistic-sized UC instances. We present the results of our investigation into modelling the UC problem as a temporal planning problem, and show how we scaled up from handling fewer than 10 generating units to more than 400, obtaining solutions almost as high quality as those generated by MIP. We conclude by discussing future directions for temporal planning in this domain, that lie beyond what can be modelled and solved using MIP methods.
    @inproceedings{Piacentini_icaps2016,
        author = "Piacentini, Chiara and Magazzeni, Daniele and Long, Derek and Fox, Maria and Dent, Chris",
        title = "{Solving Realistic Unit Commitment Problems Using Temporal Planning: Challenges and Solutions}",
        booktitle = "Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling, {ICAPS} 2016, London, UK, June 12-17, 2016.",
        pages = "421--430",
        year = "2016",
        url = "http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/12992"
    }

  • Efficient Macroscopic Urban Traffic Models for Reducing Congestion: A PDDL+ Planning Approach. Mauro Vallati, Daniele Magazzeni, Bart De Schutter, Lukás Chrpa and Thomas Leo McCluskey; Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA., pp. 3188-3194, 2016.

    Abstract
    Abstract:
    The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. In this scenario, optimising the exploitation of urban road networks is a pivotal challenge. Existing urban traffic control approaches, based on complex mathematical models, can effectively deal with planned-ahead events, but are not able to cope with unexpected situations –such as roads blocked due to car accidents or weather-related events– because of their huge computational requirements. Therefore, such unexpected situations are mainly dealt with manually, or by exploiting pre-computed policies. Our goal is to show the feasibility of using mixed discrete-continuous planning to deal with unexpected circumstances in urban traffic control. We present a PDDL+ formulation of urban traffic control, where continuous processes are used to model flows of cars, and show how planning can be used to efficiently reduce congestion of specified roads by controlling traffic light green phases. We present simulation results on two networks (one of them considers Manchester city centre) that demonstrate the effectiveness of the approach, compared with fixed-time and reactive techniques.
    @inproceedings{Vallati_aaai2016,
        author = "Vallati, Mauro and Magazzeni, Daniele and Schutter, Bart De and Chrpa, Luk{\'{a}}s and McCluskey, Thomas Leo",
        title = "{Efficient Macroscopic Urban Traffic Models for Reducing Congestion: {A} {PDDL+} Planning Approach}",
        booktitle = "Proceedings of the Thirtieth {AAAI} Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, {USA.}",
        pages = "3188--3194",
        year = "2016",
        url = "http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11985"
    }

  • Toward persistent autonomous intervention in a subsea panel. Narcís Palomeras, Arnau Carrera, Natàlia Hurtós, George C. Karras, Charalampos P. Bechlioulis, Michael Cashmore, Daniele Magazzeni, Derek Long, Maria Fox, Kostas J. Kyriakopoulos, Petar Kormushev, Joaquim Salvi and Marc Carreras; Auton. Robots, 40:1279-1306, 2016.

    Abstract
    Abstract:
    Intervention autonomous underwater vehicles (I-AUVs) have the potential to open new avenues for the maintenance and monitoring of offshore subsea facilities in a cost-effective way. However, this requires challenging intervention operations to be carried out persistently, thus minimizing human supervision and ensuring a reliable vehicle behaviour under unexpected perturbances and failures. This paper describes a system to perform autonomous intervention—in particular valve-turning—using the concept of persistent autonomy. To achieve this goal, we build a framework that integrates different disciplines, involving mechatronics, localization, control, machine learning and planning techniques, bearing in mind robustness in the implementation of all of them. We present experiments in a water tank, conducted with Girona 500 I-AUV in the context of a multiple intervention mission. Results show how the vehicle sets several valve panel configurations throughout the experiment while handling different errors, either spontaneous or induced. Finally, we report the insights gained from our experience and we discuss the main aspects that must be matured and refined in order to promote the future development of intervention autonomous vehicles that can operate, persistently, in subsea facilities.
    @article{Palomeras_autonrobots2016,
        author = "Palomeras, Narc{\'{i}}s and Carrera, Arnau and Hurt{\'{o}}s, Nat{\`{a}}lia and Karras, George C. and Bechlioulis, Charalampos P. and Cashmore, Michael and Magazzeni, Daniele and Long, Derek and Fox, Maria and Kyriakopoulos, Kostas J. and Kormushev, Petar and Salvi, Joaquim and Carreras, Marc",
        title = "{Toward persistent autonomous intervention in a subsea panel}",
        journal = "Auton. Robots",
        volume = "40",
        number = "7",
        pages = "1279--1306",
        year = "2016",
        url = "https://doi.org/10.1007/s10514-015-9511-7",
        doi = "10.1007/s10514-015-9511-7"
    }

2015

  • PDDL+ Planning with Hybrid Automata: Foundations of Translating Must Behavior. Sergiy Bogomolov, Daniele Magazzeni, Stefano Minopoli and Martin Wehrle; Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling, ICAPS 2015, Jerusalem, Israel, June 7-11, 2015., pp. 42-46, 2015.

    Abstract
    Abstract:
    Planning in hybrid domains poses a special challenge due to the involved mixed discrete-continuous dynamics. A recent solving approach for such domains is based on applying model checking techniques on a translation of PDDL+ planning problems to hybrid automata. However, the proposed translation is limited because must behavior is only over-approximated, and hence, processes and events are not reflected exactly. In this paper, we present the theoretical foundation of an exact PDDL+ translation. We propose a schema to convert a hybrid automaton with must transitions into an equivalent hybrid automaton featuring only may transitions.
    @inproceedings{Bogomolov_icaps2015,
        author = "Bogomolov, Sergiy and Magazzeni, Daniele and Minopoli, Stefano and Wehrle, Martin",
        title = "{{PDDL+} Planning with Hybrid Automata: Foundations of Translating Must Behavior}",
        booktitle = "Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling, {ICAPS} 2015, Jerusalem, Israel, June 7-11, 2015.",
        pages = "42--46",
        year = "2015",
        url = "http://www.aaai.org/ocs/index.php/ICAPS/ICAPS15/paper/view/10606"
    }

  • ROSPlan: Planning in the Robot Operating System. Michael Cashmore, Maria Fox, Derek Long, Daniele Magazzeni, Bram Ridder, Arnau Carrera, Narcís Palomeras, Natàlia Hurtós and Marc Carreras; Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling, ICAPS 2015, Jerusalem, Israel, June 7-11, 2015., pp. 333-341, 2015.

    Abstract
    Abstract:
    The Robot Operating System (ROS) is a set of software libraries and tools used to build robotic systems. ROS is known for a distributed and modular design. Given a model of the environment, task planning is concerned with the assembly of actions into a structure that is predicted to achieve goals. This can be done in a way that minimises costs, such as time or energy. Task planning is vital in directing the actions of a robotic agent in domains where a causal chain could lock the agent into a dead-end state. Moreover, planning can be used in less constrained domains to provide more intelligent behaviour. This paper describes the ROSP LAN framework, an architecture for embedding task planning into ROS systems. We provide a description of the architecture and a case study in autonomous robotics. Our case study involves autonomous underwater vehicles in scenarios that demonstrate the flexibility and robustness of our approach.
    @inproceedings{Cashmore_icaps2015,
        author = "Cashmore, Michael and Fox, Maria and Long, Derek and Magazzeni, Daniele and Ridder, Bram and Carrera, Arnau and Palomeras, Narc{\'{i}}s and Hurt{\'{o}}s, Nat{\`{a}}lia and Carreras, Marc",
        title = "{ROSPlan: Planning in the Robot Operating System}",
        booktitle = "Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling, {ICAPS} 2015, Jerusalem, Israel, June 7-11, 2015.",
        pages = "333--341",
        year = "2015",
        url = "http://www.aaai.org/ocs/index.php/ICAPS/ICAPS15/paper/view/10619"
    }

2014

  • Planning as Model Checking in Hybrid Domains. Sergiy Bogomolov, Daniele Magazzeni, Andreas Podelski and Martin Wehrle; Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 2228-2234, 2014.

    Abstract
    Abstract:
    Planning in hybrid domains is an important and challenging task, and various planning algorithms have been proposed in the last years. From an abstract point of view, hybrid planning domains are based on hybrid automata, which have been studied intensively in the model checking community. In particular, powerful model checking algorithms and tools have emerged for this formalism. However, despite the quest for more scalable planning approaches, model checking algorithms have not been applied to planning in hybrid domains so far. In this paper, we make a first step in bridging the gap between these two worlds. We provide a formal translation scheme from PDDL+ to the standard formalism of hybrid automata, as a solid basis for using hybrid system model-checking tools for dealing with hybrid planning domains. As a case study, we use the SpaceEx model checker, showing how we can address PDDL+ domains that are out of the scope of state-of-the-art planners.
    @inproceedings{Bogomolov_aaai2014,
        author = "Bogomolov, Sergiy and Magazzeni, Daniele and Podelski, Andreas and Wehrle, Martin",
        title = "{Planning as Model Checking in Hybrid Domains}",
        booktitle = "Proceedings of the 28th {AAAI} Conference on Artificial Intelligence",
        pages = "2228--2234",
        year = "2014",
        url = "http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8494"
    }

  • Policy learning for autonomous feature tracking. Daniele Magazzeni, Frédéric Py, Maria Fox, Derek Long and Kanna Rajan; Auton. Robots, 37:47-69, 2014.

    Abstract
    Abstract:
    We consider the problem of tracing the structure of oceanological features using autonomous underwater vehicles (AUVs). Solving this problem requires the construction of a control strategy that will determine the actions for the AUV based on the current state, as measured by on-board sensors and the historic trajectory (including sensed data) of the AUV. We approach this task by applying plan-based policy-learning, in which a large set of sampled problems are solved using planning and then, from the resulting plans a decision-tree is learned, using an established machine-learning algorithm, which forms the resulting policy. We evaluate our approach in simulation and report on sea trials of a prototype of a learned policy. We indicate some of the lessons learned from this deployed system and further evaluate an extended policy in simulation.
    @article{Magazzeni_autonrobots2014,
        author = "Magazzeni, Daniele and Py, Fr{\'{e}}d{\'{e}}ric and Fox, Maria and Long, Derek and Rajan, Kanna",
        title = "{Policy learning for autonomous feature tracking}",
        journal = "Auton. Robots",
        volume = "37",
        number = "1",
        pages = "47--69",
        year = "2014",
        url = "https://doi.org/10.1007/s10514-013-9375-7",
        doi = "10.1007/s10514-013-9375-7"
    }

  • AUV mission control via temporal planning. Michael Cashmore, Maria Fox, Tom Larkworthy, Derek Long and Daniele Magazzeni; 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China, May 31 - June 7, 2014, pp. 6535-6541, 2014.

    Abstract
    Abstract:
    Underwater installations require regular inspection and maintenance. We are exploring the idea of performing these tasks using an autonomous underwater vehicle, achieving persistent autonomous behaviour in order to avoid the need for frequent human intervention. In this paper we consider one aspect of this problem, which is the construction of a suitable plan for a single inspection tour. In particular we generate a temporal plan that optimises the time taken to complete the inspection mission. We report on physical trials with the system at the Diver and ROV driver Training Center in Fort William, Scotland, discussing some of the lessons learned.
    @inproceedings{Cashmore_ieee2014,
        author = "Cashmore, Michael and Fox, Maria and Larkworthy, Tom and Long, Derek and Magazzeni, Daniele",
        title = "{{AUV} mission control via temporal planning}",
        booktitle = "2014 {IEEE} International Conference on Robotics and Automation, {ICRA} 2014, Hong Kong, China, May 31 - June 7, 2014",
        pages = "6535--6541",
        year = "2014",
        url = "https://doi.org/10.1109/ICRA.2014.6907823",
        doi = "10.1109/ICRA.2014.6907823"
    }

2013

  • Challenge: Modelling Unit Commitment as a Planning Problem. Joshua Campion, Chris Dent, Maria Fox, Derek Long and Daniele Magazzeni; Proceedings of the Twenty-Third International Conference on Automated Planning and Scheduling, ICAPS 2013, Rome, Italy, June 10-14, 2013, 2013.

    Abstract
    Abstract:
    Unit Commitment is a fundamental problem in power systems engineering, deciding which generating units to switch on, and when to switch them on, in order to efficiently meet anticipated demand. It has traditionally been solved as a Mixed Integer Programming (MIP) problem but upcoming changes to the power system drastically increase the MIP solution time. In this paper, we discuss the benefits that using planning may have over the established methods.We provide a formal description of Unit Commitment, and we present its formulation as MIP and as a planning problem. This is a novel and interesting application area for planning, with features that make the domain challenging for current planners.
    @inproceedings{Campion_aaai2013,
        author = "Campion, Joshua and Dent, Chris and Fox, Maria and Long, Derek and Magazzeni, Daniele",
        title = "{Challenge: Modelling Unit Commitment as a Planning Problem}",
        booktitle = "Proceedings of the Twenty-Third International Conference on Automated Planning and Scheduling, {ICAPS} 2013, Rome, Italy, June 10-14, 2013",
        year = "2013",
        url = "http://www.aaai.org/ocs/index.php/ICAPS/ICAPS13/paper/view/6041"
    }

  • CGMurphi: Automatic synthesis of numerical controllers for nonlinear hybrid systems. Giuseppe Della Penna, Benedetto Intrigila, Daniele Magazzeni, Igor Melatti and Enrico Tronci; Eur. J. Control, 19:14-36, 2013.

    Abstract
    Abstract:
    In the last years, the use of controllers has become very common, thus much work is being done to create automatic controller synthesis tools. When dealing with critical systems, most of the times such controllers are required to be optimal and robust, i.e., they must achieve their goal with minimal resource consumption and be able to handle also unexpected situations. All these requirements, which are intrinsically difficult to satisfy, become even more challenging when dealing with hybrid systems, which represent a wide range of real world systems. In this paper we propose a model checking based tool, namely CGMurphi, which assists in the generation of optimal and robust numerical controllers for systems having complex dynamics, possibly hybrid systems. The tool provides a complete controller generation solution, being also able to effectively compress the controllers and encode them so that they can be directly embedded in software/hardware systems. The tool has been widely experimented with very promising results. In particular, the present paper reports the complete experimentation results relative to two academic case studies, and the preliminary achievements obtained by applying CGMurphi to an industrial critical system.
    @article{Penna_eurjcontrol2013,
        author = "{Della Penna}, Giuseppe and Intrigila, Benedetto and Magazzeni, Daniele and Melatti, Igor and Tronci, Enrico",
        title = "{CGMurphi: Automatic synthesis of numerical controllers for nonlinear hybrid systems}",
        journal = "Eur. J. Control",
        volume = "19",
        number = "1",
        pages = "14--36",
        year = "2013",
        url = "https://doi.org/10.1016/j.ejcon.2013.02.001",
        doi = "10.1016/j.ejcon.2013.02.001"
    }

2012

  • Plan-Based Policy-Learning for Autonomous Feature Tracking. Maria Fox, Derek Long and Daniele Magazzeni; Proceedings of the Twenty-Second International Conference on Automated Planning and Scheduling, ICAPS 2012, 2012.

    Abstract
    Abstract:
    Mapping and tracking biological ocean features, such as harmful algal blooms, is an important problem in the environmental sciences. The problem exhibits a high degree of uncertainty, because of both the dynamic ocean context and the challenges of sensing. Plan-based policy learning has been shown to be a powerful technique for obtaining robust intelligent behaviour in the face of uncertainty. In this paper we apply this technique in simulation, to the problem of tracking the outer edge of 2D biological features, such as the surfaces of harmful algal blooms. We show that plan-based policylearning leads to highly accurate tracking in simulation, even in situations where the uncertainty governing the shape of the patch cannot be directly modelled. We present simulation results that give confidence that the approach could work in practice. We are now collaborating with ocean scientists at MBARI to perform physical tests at sea.
    @inproceedings{FoxL_icaps2012,
        author = "Fox, Maria and Long, Derek and Magazzeni, Daniele",
        title = "{Plan-Based Policy-Learning for Autonomous Feature Tracking}",
        booktitle = "Proceedings of the Twenty-Second International Conference on Automated Planning and Scheduling, {ICAPS} 2012",
        year = "2012",
        url = "http://www.aaai.org/ocs/index.php/ICAPS/ICAPS12/paper/view/4694"
    }

  • A universal planning system for hybrid domains. Giuseppe Della Penna, Daniele Magazzeni and Fabio Mercorio; Appl. Intell., 36:932-959, 2012.

    Abstract
    Abstract:
    Many real world problems involve hybrid systems, subject to (continuous) physical effects and controlled by (discrete) digital equipments. Indeed, many efforts are being made to extend the current planning systems and modelling languages to support such kind of domains. However, hybrid systems often present also a nonlinear behaviour and planning with continuous nonlinear change that is still a challenging issue. In this paper we present the UPMurphi tool, a universal planner based on the discretise and validate approach that is capable of reasoning with mixed discrete/continuous domains, fully respecting the semantics of PDDL+. Given an initial discretisation, the hybrid system is discretised and given as input to UPMurphi, which performs universal planning on such an approximated model and checks the correctness of the results. If the validation fails, the approach is repeated by appropriately refining the discretisation. To show the effectiveness of our approach, the paper presents two real hybrid domains where universal planning has been successfully performed using the UPMurphi tool.
    @article{Penna_applintell2012,
        author = "{Della Penna}, Giuseppe and Magazzeni, Daniele and Mercorio, Fabio",
        title = "{A universal planning system for hybrid domains}",
        journal = "Appl. Intell.",
        volume = "36",
        number = "4",
        pages = "932--959",
        year = "2012",
        url = "https://doi.org/10.1007/s10489-011-0306-z",
        doi = "10.1007/s10489-011-0306-z"
    }

  • Plan-based Policies for Efficient Multiple Battery Load Management. Maria Fox, Derek Long and Daniele Magazzeni; J. Artif. Intell. Res., 44:335-382, 2012.

    Abstract
    Abstract:
    Efficient use of multiple batteries is a practical problem with wide and growing application.The problem can be cast as a planning problem under uncertainty. We describe the approach wehave adopted to modelling and solving this problem, seen as a Markov Decision Problem, buildingeffective policies for battery switching in the face of stochastic load profiles. Our solution exploits and adapts several existing techniques: planning for deterministic mixeddiscrete-continuous problems and Monte Carlo sampling for policy learning. The paper describesthe development of planning techniques to allow solution of the non-linear continuous dynamicmodels capturing the battery behaviours. This approach depends on carefully handled discretisa-tion of the temporal dimension. The construction of policies is performed using a classificationapproach and this idea offers opportunities for wider exploitation in other problems. The approachand its generality are described in the paper. Application of the approach leads to construction of policies that, in simulation, significantlyoutperform those that are currently in use and the best published solutions to the battery manage-ment problem. We achieve solutions that achieve more than 99% efficiency in simulation comparedwith the theoretical limit and do so with far fewer battery switches than existing policies. Behaviourof physical batteries does not exactly match the simulated models for many reasons, so to confirmthat our theoretical results can lead to real measured improvements in performance we also conductand report experiments using a physical test system. These results demonstrate that we can obtain5%-15% improvement in lifetimes in the case of a two battery system.
    @article{Fox_jartifintellres2012,
        author = "Fox, Maria and Long, Derek and Magazzeni, Daniele",
        title = "{Plan-based Policies for Efficient Multiple Battery Load Management}",
        journal = "J. Artif. Intell. Res.",
        volume = "44",
        pages = "335--382",
        year = "2012",
        url = "https://doi.org/10.1613/jair.3643",
        doi = "10.1613/jair.3643"
    }

2011

  • A framework for the automatic synthesis of hybrid fuzzy/numerical controllers. Daniele Magazzeni; Appl. Soft Comput., 11:276-284, 2011.

    Abstract
    Abstract:
    Mapping and tracking biological ocean features, such as harmful algal blooms, is an important problem in the environmental sciences. The problem exhibits a high degree of uncertainty, because of both the dynamic ocean context and the challenges of sensing. Plan-based policy learning has been shown to be a powerful technique for obtaining robust intelligent behaviour in the face of uncertainty. In this paper we apply this technique in simulation, to the problem of tracking the outer edge of 2D biological features, such as the surfaces of harmful algal blooms. We show that plan-based policy-learning leads to highly accurate tracking in simulation, even in situations where the uncertainty governing the shape of the patch cannot be directly modelled. We present simulation results that give confidence that the approach could work in practice. We are now collaborating with ocean scientists at MBARI to perform physical tests at sea.
    @article{Magazzeni_softcom2011,
        author = "Magazzeni, Daniele",
        title = "{A framework for the automatic synthesis of hybrid fuzzy/numerical controllers}",
        journal = "Appl. Soft Comput.",
        volume = "11",
        number = "1",
        pages = "276--284",
        year = "2011",
        url = "https://doi.org/10.1016/j.asoc.2009.11.018",
        doi = "10.1016/j.asoc.2009.11.018"
    }

  • Automatic Construction of Efficient Multiple Battery Usage Policies. Maria Fox, Derek Long and Daniele Magazzeni; Proceedings of the 21st International Conference on Automated Planning and Scheduling, ICAPS 2011, Freiburg, Germany June 11-16, 2011, 2011.

    Abstract
    Abstract:
    Efficient use of multiple batteries is a practical problem with wide and growing application. The problem can be cast as a planning problem. We describe the approach we have adopted to modelling and solving this problem, seen as a Markov Decision Problem, building effective policies for battery switching in the face of stochastic load profiles. Our solution exploits and adapts several existing techniques from the planning literature and leads to the construction of policies that significantly outperform those that are currently in use and the best published solutions to the battery management problem. We achieve solutions that achieve more than 99% efficiency compared with the theoretical limit and do so with far fewer battery switches than existing policies. We describe the approach in detail and provide empirical evaluation demonstrating its effectiveness.
    @inproceedings{Fox_icaps2011,
        author = "Fox, Maria and Long, Derek and Magazzeni, Daniele",
        title = "{Automatic Construction of Efficient Multiple Battery Usage Policies}",
        booktitle = "Proceedings of the 21st International Conference on Automated Planning and Scheduling, {ICAPS} 2011, Freiburg, Germany June 11-16, 2011",
        year = "2011",
        url = "http://aaai.org/ocs/index.php/ICAPS/ICAPS11/paper/view/2683"
    }

2010

  • A PDDL+ Benchmark Problem: The Batch Chemical Plant. Giuseppe Della Penna, Benedetto Intrigila, Daniele Magazzeni and Fabio Mercorio; Proceedings of the 20th International Conference on Automated Planning and Scheduling, ICAPS 2010, Toronto, Ontario, Canada, May 12-16, 2010, pp. 222-225, 2010.

    Abstract
    Abstract:
    The PDDL+ language has been mainly devised to allow modelling of real-world systems, with continuous, time-dependant dynamics. Several interesting case studies with these characteristics have been also proposed, to test the language expressiveness and the capabilities of the support tools. However, most of these case studies have not been completely developed so far. In this paper we focus on the batch chemical plant case study, a very complex hybrid system with nonlinear dynamics that could represent a challenging benchmark problem for planning techniques and tools. We present a complete PDDL+ model for such system, and show an example application where the UPMurphi universal planner is used to generate a set of production policies for the plant.
    @inproceedings{Penna_icaps2010,
        author = "{Della Penna}, Giuseppe and Intrigila, Benedetto and Magazzeni, Daniele and Mercorio, Fabio",
        title = "{A {PDDL+} Benchmark Problem: The Batch Chemical Plant}",
        booktitle = "Proceedings of the 20th International Conference on Automated Planning and Scheduling, {ICAPS} 2010, Toronto, Ontario, Canada, May 12-16, 2010",
        pages = "222--225",
        year = "2010",
        url = "http://www.aaai.org/ocs/index.php/ICAPS/ICAPS10/paper/view/1418"
    }

2009

  • UPMurphi: A Tool for Universal Planning on PDDL+ Problems. Giuseppe Della Penna, Daniele Magazzeni, Fabio Mercorio and Benedetto Intrigila; Proceedings of the 19th International Conference on Automated Planning and Scheduling, ICAPS 2009, Thessaloniki, Greece, September 19-23, 2009, 2009.

    Abstract
    Abstract:
    Systems subject to (continuous) physical effects and controlled by (discrete) digital equipments, are today very common. Thus, many realistic domains where planning is required are represented by hybrid systems, i.e., systems containing both discrete and continuous values, with possibly a nonlinear continuous dynamics. The PDDL+ language allows one to model these domains, however the current tools can generally handle only planning problems on (possibly hybrid) systems with linear dynamics. Therefore, universal planning applied to hybrid systems and, in general, to non-linear systems is completely out of scope for such tools. In this paper, we propose the use of explicit model checking-based techniques to solve universal planning problems on such hardly-approachable domains.
    @inproceedings{Penna_icaps2009,
        author = "{Della Penna}, Giuseppe and Magazzeni, Daniele and Mercorio, Fabio and Intrigila, Benedetto",
        title = "{UPMurphi: {A} Tool for Universal Planning on {PDDL+} Problems}",
        booktitle = "Proceedings of the 19th International Conference on Automated Planning and Scheduling, {ICAPS} 2009, Thessaloniki, Greece, September 19-23, 2009",
        year = "2009",
        url = "http://aaai.org/ocs/index.php/ICAPS/ICAPS09/paper/view/707"
    }