187 research outputs found

    Multi-Agent Planning with Planning Graph

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    In this paper, we consider planning for multi-agents situations in STRIPS-like domains with planning graph. Three possible relationships between agents' goals are considered in order to evaluate plans: the agents may be collaborative, adversarial or indifferent entities. We propose algorithms to deal with each situation. The collaborative situations can be easily dealt with the original Graphplan algorithm by redefining the domain in a proper way. Forward-chaining and backward chaining algorithms are discussed to find infallible plans in adversarial situations. In case such plans cannot be found, the agent can still attempt to find a plan for achieving some part of the goals. A forward-chaining algorithm is also proposed to find plans for agents with independent goals

    Planning in probabilistic domains using a deterministic numeric planner

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    In the probabilistic track of the IPC5 - the last International planning competitions - a probabilistic planner based on combining deterministic planning with replanning - FF-REPLAN - out performed the other competitors. This probabilistic planning paradigm discarded the probabilistic information of the domain, just considering for each action its nominal effect as a deterministic effect

    Contingent planning under uncertainty via stochastic satisfiability

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    We describe a new planning technique that efficiently solves probabilistic propositional contingent planning problems by converting them into instances of stochastic satisfiability (SSAT) and solving these problems instead. We make fundamental contributions in two areas: the solution of SSAT problems and the solution of stochastic planning problems. This is the first work extending the planning-as-satisfiability paradigm to stochastic domains. Our planner, ZANDER, can solve arbitrary, goal-oriented, finite-horizon partially observable Markov decision processes (POMDPs). An empirical study comparing ZANDER to seven other leading planners shows that its performance is competitive on a range of problems. © 2003 Elsevier Science B.V. All rights reserved

    Computing action equivalences for planning under time-constraints

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    In order for autonomous artificial decision-makers to solverealistic tasks, they need to deal with the dual problems of searching throughlarge state and action spaces under time pressure.We study the problem of planning in domains with lots of objects. Structuredrepresentations of action can help provide guidance when the number of actionchoices and size of the state space is large.We show how structured representations ofaction effects can help us partition the action space in to a smallerset of approximate equivalence classes. Then, the pared-downaction space can be used to identify a useful subset of the state space in whichto search for a solution. As computational resources permit, we thenallow ourselves to elaborate the original solution. This kind of analysisallows us to collapse the action space and permits faster planning in muchlarger domains than before

    Combining task and motion planning for mobile manipulators

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    Aplicat embargament des de la data de defensa fins el dia 31/12/2019Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit d’Enginyeria IndustrialThis thesis addresses the combination of task and motion planning which deals with different types of robotic manipulation problems. Manipulation problems are referred to as mobile manipulation, collaborative multiple mobile robots tasks, and even higher dimensional tasks (like bi-manual robots or mobile manipulators). Task and motion planning problems needs to obtain a geometrically feasible manipulation plan through symbolic and geometric search space. The combination of task and motion planning levels has emerged as a challenging issue as the failure leads robots to dead-end tasks due to geometric constraints. In addition, task planning is combined with physics-based motion planning and information to cope with manipulation tasks in which interactions between robots and objects are required, or also a low-cost feasible plan in terms of power is looked for. Moreover, combining task and motion planning frameworks is enriched by introducing manipulation knowledge. It facilitates the planning process and aids to provide the way of executing symbolic actions. Combining task and motion planning can be considered under uncertain information and with human-interaction. Uncertainty can be viewed in the initial state of the robot world or the result of symbolic actions. To deal with such issues, contingent-based task and motion planning is proposed using a perception system and human knowledge. Also, robots can ask human for those tasks which are difficult or infeasible for the purpose of collaboration. An implementation framework to combine different types of task and motion planning is presented. All the required modules and tools are also illustrated. As some task planning algorithms are implemented in Prolog or C++ languages and our geometric reasoner is developed in C++, the flow of information between different languages is explained.Aquesta tesis es centra en les eines de planificació combinada a nivell de tasca i a nivell de moviments per abordar diferents problemes de manipulació robòtica. Els problemes considerats són de navegació de robots mòbil enmig de obstacles no fixes, tasques de manipulació cooperativa entre varis robots mòbils, i tasques de manipulació de dimensió més elevada com les portades a terme amb robots bi-braç o manipuladors mòbils. La planificació combinada de tasques i de moviments ha de cercar un pla de manipulació que sigui geomètricament realitzable, a través de d'un espai de cerca simbòlic i geomètric. La combinació dels nivells de planificació de tasca i de moviments ha sorgit com un repte ja que les fallades degudes a les restriccions geomètriques poden portar a tasques sense solució. Addicionalment, la planificació a nivell de tasca es combina amb informació de la física de l'entorn i amb mètodes de planificació basats en la física, per abordar tasques de manipulació en les que la interacció entre el robot i els objectes és necessària, o també si es busca un pla realitzable i amb un baix cost en termes de potència. A més, el marc proposat per al combinació de la planificació a nivell de tasca i a nivell de moviments es millora mitjançant l'ús de coneixement, que facilita el procés de planificació i ajuda a trobar la forma d'executar accions simbòliques. La combinació de nivells de planificació també es pot considerar en casos d'informació incompleta i en la interacció humà-robot. La incertesa es considera en l'estat inicial i en el resultat de les accions simbòliques. Per abordar aquest problema, es proposa la planificació basada en contingències usant un sistema de percepció i el coneixement de l'operari humà. Igualment, els robots poden demanar col·laboració a l'operari humà per a que realitzi aquelles accions que són difícils o no realitzables pel robot. Es presenta també un marc d'implementació per a la combinació de nivells de planificació usant diferents mètodes, incloent tots els mòduls i eines necessàries. Com que alguns algorismes estan implementats en Prolog i d'altres en C++, i el mòdul de raonament geomètric proposat està desenvolupat en C++, es detalla el flux d'informació entre diferents llenguatges.Award-winningPostprint (published version

    Knowledge-Based Task Structure Planning for an Information Gathering Agent

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    An effective solution to model and apply planning domain knowledge for deliberation and action in probabilistic, agent-oriented control is presented. Specifically, the addition of a task structure planning component and supporting components to an agent-oriented architecture and agent implementation is described. For agent control in risky or uncertain environments, an approach and method of goal reduction to task plan sets and schedules of action is presented. Additionally, some issues related to component-wise, situation-dependent control of a task planning agent that schedules its tasks separately from planning them are motivated and discussed
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