Raffinement des intentions

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Temporal and Hierarchical Models for Planning and Acting in Robotics

The field of AI planning has seen rapid progress over the last decade and planners are now able to find plan with hundreds of actions in a matter of seconds. Despite those important progresses, robotic systems still tend to have a reactive architecture with very little deliberation on the course of the plan they might follow. In this thesis, we argue that a successful integration with a robotic system requires the planner to have capacities for both temporal and hierarchical reasoning. The former is indeed a universal resource central in many robot activities while the latter is a critical component for the integration of reasoning capabilities at different abstraction levels, typically starting with a high level view of an activity that is iteratively refined down to motion primitives. As a first step to carry out this vision, we present a model for temporal planning unifying the generative and hierarchical approaches. At the center of the model are temporal action templates, similar to those of PDDL complemented with a specification of the initial state as well as the expected evolution of the environment over time. In addition, our model allows for the specification of hierarchical knowledge possibly with a partial coverage. Consequently, our model generalizes the existing generative and HTN approaches together with an explicit time representation. In the second chapter, we introduce a planning procedure suitable for our planning model. In order to support hierarchical features, we extend the existing Partial-Order Causal Link approach used in many constraintbased planners, with the notions of task and decomposition. We implement it in FAPE (Flexible Acting and Planning Environment) together with automated problem analysis techniques used for search guidance. We show FAPE to have performance similar to state of the art temporal planners when used in a generative setting. The addition of hierarchical information leads to further performance gain and allows us to outperform traditional planners. In the third chapter, we study the usual methods used to reason on temporal uncertainty while planning. We relax the usual assumption of total observability and instead provide techniques to reason on the observations needed to maintain a plan dispatchable. We show how such needed observations can be detected at planning time and incrementally dealt with by considering the appropriate sensing actions. In a final chapter, we discuss the place of the proposed planning system as a central component for the control of a robotic actor. We demonstrate how the explicit time representation facilitates plan monitoring and action dispatching when dealing with contingent events that require observation. We take advantage of the constraint-based and hierarchical representation to facilitate both plan-repair procedures as well opportunistic plan refinement at acting time

HTN-Like Solutions for Classical Planning Problems: An Application to BDI Agent Systems

In this paper we explore the question of what characterises a desirable plan of action and how such a plan could be computed, in the context of systems that already possess a certain amount of hierarchical domain knowledge. In contrast to past work in this setting, which focuses on generating low-level plans, losing much of the domain knowledge inherent in such systems, we argue that plans ought to be HTN-like or abstract, i.e., re-use and respect the user-supplied know-how in the underlying domain. In doing so, we recognise an intrinsic tension between striving for abstract plans but ensuring that unnecessary actions, not linked to the specific goal to be achieved, are avoided. We explore this tension by characterising the set of “ideal” abstract plans that are non-redundant but maximally abstract, and then develop a more limited yet feasible account in which a given (arbitrary) abstract plan is “specialised” into one such non-redundant plan that is as abstract as possible. We present an algorithm that can compute such specialisations, and analyse the theoretical properties of our proposal

Lilotane : A Lifted SAT-based Approach to Hierarchical Planning

One of the oldest and most popular approaches to automated planning is to encode the problem at hand into a propositional formula and use a Satisfiability (SAT) solver to find a solution. In all established SAT-based approaches for Hierarchical Task Network (HTN) planning, grounding the problem is necessary and oftentimes introduces a combinatorial blowup in terms of the number of actions and reductions to encode. Our contribution named Lilotane (Lifted Logic for Task Networks) eliminates this issue for Totally Ordered HTN planning by directly encoding the lifted representation of the problem at hand. We lazily instantiate the problem hierarchy layer by layer and use a novel SAT encoding which allows us to defer decisions regarding method arguments to the stage of SAT solving. We show the correctness of our encoding and compare it to the best performing prior SAT encoding in a worst-case analysis. Empirical evaluations confirm that Lilotane outperforms established SAT-based approaches, often by orders of magnitude, produces much smaller formulae on average, and compares favorably to other state-of-the-art HTN planners regarding robustness and plan quality. In the International Planning Competition (IPC) 2020, a preliminary version of Lilotane scored the second place. We expect these considerable improvements to SAT-based HTN planning to open up new perspectives for SAT-based approaches in related problem classes

Automated Hierarchical, Forward-Chaining Temporal Planner for Planetary Robots Exploring Unknown Environments

The transition of mobile robots from a controlled environment towards the real-world represents a major leap in terms of complexity coming primarily from three different factors: partial observability, nondeterminism and dynamic events. To cope with them, robots must achieve some intelligence behaviours to be cost and operationally effective. Two particularly interesting examples of highly complex robotic scenarios are Mars rover missions and the Darpa Robotic Challenge (DRC). In spite of the important differences they present in terms of constraints and requirements, they both have adopted certain level of autonomy to overcome some specific problems. For instance, Mars rovers have been endowed with multiple systems to enable autonomous payload operations and consequently increase science return. In the case of DRC, most teams have autonomous footstep planning or arm trajectory calculation. Even though some specific problems can be addressed with dedicated tools, the general problem remains unsolved: to deploy on-board a reliable reasoning system able to operate robots without human intervention even in complex environments. This is precisely the goal of an automated mission planner. The scientific community has provided plenty of planners able to provide very fast solutions for classical problems, typically characterized by the lack of time and resources representation. Moreover, there are also a handful of applied planners with higher levels of expressiveness at the price of lowest performance. However, a fast, expressive and robust planner has never been used in complex robotic missions. These three properties represent the main drivers for the outcomes of the thesis. To bridge the gap between classical and applied planning, a novel formalism named Hierarchical TimeLine Networks (HTLN) combining Timeline and HTN planning has been proposed. HTLN has been implemented on a mission planner named QuijoteExpress, the first forward-chaining timeline planner to the best of our knowledge. The main idea is to benefit from the great performance of forward-chaining search to resolve temporal problems on the state-space. In addition, QuijoteExpress includes search enhancements such as parallel planning by division of the problem in sub-problems or advanced heuristics management. Regarding expressiveness, the planner incorporates HTN techniques that allow to define hierarchical models and solutions. Finally, plan robustness in uncertain scenarios has been addressed by means of sufficient plans that allow to leave parts of valid plans undefined. To test the planner, a novel lightweight, timeline and ROS-based executive named SanchoExpress has been designed to translate the plans into actions understandable by the different robot subsystems. The entire approach has been tested in two realistic and complementary domains. A cooperative multirover Mars mission and an urban search and rescue mission. The results were extremely positive and opens new promising ways in the field of automated planning applied to robotics

CAMP-BDI: an approach for multiagent systems robustness through capability-aware agents maintaining plans

Rational agent behaviour is frequently achieved through the use of plans, particularly within the widely used BDI (Belief-Desire-Intention) model for intelligent agents. As a consequence, preventing or handling failure of planned activity is a vital component in building robust multiagent systems; this is especially true in realistic environments, where unpredictable exogenous change during plan execution may threaten intended activities. Although reactive approaches can be employed to respond to activity failure through replanning or plan-repair, failure may have debilitative effects that act to stymie recovery and, potentially, hinder subsequent activity. A further factor is that BDI agents typically employ deterministic world and plan models, as probabilistic planning methods are typical intractable in realistically complex environments. However, deterministic operator preconditions may fail to represent world states which increase the risk of activity failure. The primary contribution of this thesis is the algorithmic design of the CAMP-BDI (Capability Aware, Maintaining Plans) approach; a modification of the BDI reasoning cycle which provides agents with beliefs and introspective reasoning to anticipate increased risk of failure and pro-actively modify intended plans in response. We define a capability meta-knowledge model, providing information to identify and address threats to activity success using precondition modelling and quantitative quality estimation. This also facilitates semantic-independent communication of capability information for general advertisement and of dependency information - we define use of the latter, within a structured messaging approach, to extend local agent algorithms towards decentralized, distributed robustness. Finally, we define a policy based approach for dynamic modification of maintenance behaviour, allowing response to observations made during runtime and with potential to improve re-usability of agents in alternate environments. An implementation of CAMP-BDI is compared against an equivalent reactive system through experimentation in multiple perturbation configurations, using a logistics domain. Our empirical evaluation indicates CAMP-BDI has significant benefit if activity failure carries a strong risk of debilitative consequence

Agent-Based Algorithms for the Vehicle Routing Problem with Time Windows

Vehicle routing problem s casovymi okny (VRPTW) je jednim z nejdulezitSjSich a nejvice zkou- manych problemu v oblasti dopravy. Matematicky model tohoto problemu vystihuje klicove vlastnosti spolecne cele fadS dalslch dopravmch problemu feSenych v praxi. Jadrem problemu je hledani mnoziny tras zacmajicicli a koncicich v jedinem depu, ktere obsahuji zastavky u mnoziny zakazniku. Pro kazdSho zakazm'ka je pak definovano konkretm' mnozstvf zbozf, jez je tfeba dorucit a casove okno, ve kterem je pozadovano dodani tohoto zbozi. Realne aplikace tohoto problemu jsou zpravidla vyrazne bohatsi, napojene na nadfazene logisticke systemy. KliSoA'ym faktorem pro uspSSne nasazeni odpovldajicich algoritmu je proto jejich fiexibilita vzhledem k dodatecnym rozSuemm zhkladmho matematickeho modelu spojenym s nasazenim v realnem sv§t§. Dalglm podstatnym faktorem je schopnost systemu reagovat na nepfedvidane udalosti jako jsou dopravm zaepy, poruchy, zmgny preferenci zakazniku atd. Multi-agentni systemy reprezentuji architekturu a navrhovy vzor vhodny pro modelovani heterogennlch a dynamickych systemu. Entity v systemu jsou v ramci multi-agentmho mo- delu reprezentovany mnozinou agentil s odpovidajlci'mi vzorci autonommho jako i spolecenskeho chovani. Chovani systemu jako celku pak vyplyva z autonomnich akci...The vehicle routing problem with time windows (VRPTW) is one of the most important and widely studied transportation optimization problems. It abstracts the salient features of numer- ous distribution related real-world problems. It is a problem of finding a set of routes starting and ending at a single depot serving a set of geographically scattered customers, each within a specific time-window and with a specific demand of goods to be delivered. The real world applications of the VRPTW can be very complex being part of higher level sj'^stems i.e. complex supply chain management solutions. For a successful deployment it is impor- tant for these systems to be flexible in terms of incorporating the problem specific side-constraints and problem extensions in an elegant way. Also, employing efficient means of addressing the dy- namism inherent to the execution phase of the relevant operations is vital. The multi-agent systems are an emerging architectm-e with respect to modeling multi-actor heterogenous and dynamic environments. The entities within the system are represented by a set of agents endowed with autonomic as well as social behavioral patterns. The behavior of the system then emerges from their actions and interactions. The autonomic nature of such a model makes it very robust in highly...Katedra softwarového inženýrstvíDepartment of Software EngineeringFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

A Cognitive Robotic Imitation Learning System Based On Cause-Effect Reasoning

As autonomous systems become more intelligent and ubiquitous, it is increasingly important that their behavior can be easily controlled and understood by human end users. Robotic imitation learning has emerged as a useful paradigm for meeting this challenge. However, much of the research in this area focuses on mimicking the precise low-level motor control of a demonstrator, rather than interpreting the intentions of a demonstrator at a cognitive level, which limits the ability of these systems to generalize. In particular, cause-effect reasoning is an important component of human cognition that is under-represented in these systems. This dissertation contributes a novel framework for cognitive-level imitation learning that uses parsimonious cause-effect reasoning to generalize demonstrated skills, and to justify its own actions to end users. The contributions include new causal inference algorithms, which are shown formally to be correct and have reasonable computational complexity characteristics. Additionally, empirical validations both in simulation and on board a physical robot show that this approach can efficiently and often successfully infer a demonstrator’s intentions on the basis of a single demonstration, and can generalize learned skills to a variety of new situations. Lastly, computer experiments are used to compare several formal criteria of parsimony in the context of causal intention inference, and a new criterion proposed in this work is shown to compare favorably with more traditional ones. In addition, this dissertation takes strides towards a purely neurocomputational implementation of this causally-driven imitation learning framework. In particular, it contributes a novel method for systematically locating fixed points in recurrent neural networks. Fixed points are relevant to recent work on neural networks that can be “programmed” to exhibit cognitive-level behaviors, like those involved in the imitation learning system developed here. As such, the fixed point solver developed in this work is a tool that can be used to improve our engineering and understanding of neurocomputational cognitive control in the next generation of autonomous systems, ultimately resulting in systems that are more pliable and transparent