165 research outputs found

    A dynamic epistemic framework for reasoning about conformant probabilistic plans

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    In this paper, we introduce a probabilistic dynamic epistemic logical framework that can be applied for reasoning and verifying conformant probabilistic plans in a single agent setting. In conformant probabilistic planning (CPP), we are looking for a linear plan such that the probability of achieving the goal after executing the plan is no less than a given threshold probability δ. Our logical framework can trace the change of the belief state of the agent during the execution of the plan and verify the conformant plans. Moreover, with this logic, we can enrich the CPP framework by formulating the goal as a formula in our language with action modalities and probabilistic beliefs. As for the main technical results, we provide a complete axiomatization of the logic and show the decidability of its validity problem

    Planning and learning under uncertainty

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    Automated Planning is the component of Artificial Intelligence that studies the computational process of synthesizing sets of actions whose execution achieves some given objectives. Research on Automated Planning has traditionally focused on solving theoretical problems in controlled environments. In such environments both, the current state of the environment and the outcome of actions, are completely known. The development of real planning applications during the last decade (planning fire extinction operations (Castillo et al., 2006), planning spacecraft activities (Nayak et al., 1999), planning emergency evacuation actions (Muñoz-Avila et al., 1999) has evidenced that these two assumptions are not true in many real-world problems. The planning research community is aware of this issue and during the last years, it has multiply its efforts to find new planning systems able to address these kinds of problems. All these efforts have created a new field in Automated Planning called planning under uncertainty. Nevertheless, the new systems suffer from two limitations. (1) They precise accurate action models, though the definition by hand of accurate action models is frequently very complex. (2) They present scalability problems due to the combinatorial explosion implied by the expressiveness of its action models. This thesis defines a new planning paradigm for building, in an efficient and scalable way, robust plans in domains with uncertainty though the action model is incomplete. The thesis is that, the integration of relational machine learning techniques with the planning and execution processes, allows to develop planning systems that automatically enrich their initial knowledge about the environment and therefore find more robust plans. An empirical evaluation illustrates these benefits in comparison with state-of-the-art probabilistic planners which use static actions models. -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------La Planificación Automática es la rama de la Inteligencia Artificial que estudia los procesos computacionales para la síntesis de conjuntos de acciones cuya ejecución permita alcanzar unos objetivos dados. Históricamente, la investigación en esta rama ha tratado de resolver problemas teóricos en entornos controlados en los que conocía tanto el estado actual del entorno como el resultado de ejecutar acciones en él. En la última década, el desarrollo de aplicaciones de planificación (gestión de las tareas de extinción de incendios forestales (Castillo et al., 2006), control de las actividades de la nave espacial Deep Space 1 (Nayak et al., 1999), planificación de evacuaciones de emergencia (Muñoz-Avila et al., 1999) ha evidenciado que tales supuestos no son ciertos en muchos problemas reales. Consciente de ello, la comunidad investigadora ha multiplicado sus esfuerzos para encontrar nuevos paradigmas de planificación que se ajusten mejor a este tipo de problemas. Estos esfuerzos han llevado al nacimiento de una nueva área dentro de la Planificación Automática, llamada planificación con incertidumbre. Sin embargo, los nuevos planificadores para dominios con incertidumbre aún presentan dos importantes limitaciones: (1) Necesitan modelos de acciones detallados que contemplen los posibles resultados de ejecutar cada acción. En la mayoría de problemas reales es difícil obtener modelos de este tipo. (2) Presentan fuertes problemas de escalabilidad debido a la explosión combinatoria que provoca la complejidad de los modelos de acciones que manejan. En esta Tesis se define un paradigma de planificación capaz de generar, de forma eficiente y escalable, planes robustos en dominios con incertidumbre aunque no se disponga de modelos de acciones completamente detallados. La Tesis que se defiende es que la integración de técnicas de aprendizaje automático relacional con los procesos de decisión y ejecución permite desarrollar sistemas de planificación capaces de enriquecer automáticamente su modelo de acciones con información adicional que les ayuda a encontrar planes más robustos. Los beneficios de esta integración son evaluados experimentalmente mediante una comparación con planificadores probabilísticos del estado del arte los cuales no modifican su modelo de acciones

    Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting

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    We present a new algorithm for probabilistic planning with no observability. Our algorithm, called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-FF to problems with probabilistic uncertainty about both the initial state and action effects. Specifically, Probabilistic-FF combines Conformant-FFs techniques with a powerful machinery for weighted model counting in (weighted) CNFs, serving to elegantly define both the search space and the heuristic function. Our evaluation of Probabilistic-FF shows its fine scalability in a range of probabilistic domains, constituting a several orders of magnitude improvement over previous results in this area. We use a problematic case to point out the main open issue to be addressed by further research

    ICAPS 2012. Proceedings of the third Workshop on the International Planning Competition

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    22nd International Conference on Automated Planning and Scheduling. June 25-29, 2012, Atibaia, Sao Paulo (Brazil). Proceedings of the 3rd the International Planning CompetitionThe Academic Advising Planning Domain / Joshua T. Guerin, Josiah P. Hanna, Libby Ferland, Nicholas Mattei, and Judy Goldsmith. -- Leveraging Classical Planners through Translations / Ronen I. Brafman, Guy Shani, and Ran Taig. -- Advances in BDD Search: Filtering, Partitioning, and Bidirectionally Blind / Stefan Edelkamp, Peter Kissmann, and Álvaro Torralba. -- A Multi-Agent Extension of PDDL3.1 / Daniel L. Kovacs. -- Mining IPC-2011 Results / Isabel Cenamor, Tomás de la Rosa, and Fernando Fernández. -- How Good is the Performance of the Best Portfolio in IPC-2011? / Sergio Nuñez, Daniel Borrajo, and Carlos Linares López. -- “Type Problem in Domain Description!” or, Outsiders’ Suggestions for PDDL Improvement / Robert P. Goldman and Peter KellerEn prens

    AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

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    Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an execution thread which orchestrates these planning requests as well as action execution and state monitoring. We show how the behavior of the execution thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop

    On the Role of Canonicity in Bottom-up Knowledge Compilation

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    We consider the problem of bottom-up compilation of knowledge bases, which is usually predicated on the existence of a polytime function for combining compilations using Boolean operators (usually called an Apply function). While such a polytime Apply function is known to exist for certain languages (e.g., OBDDs) and not exist for others (e.g., DNNF), its existence for certain languages remains unknown. Among the latter is the recently introduced language of Sentential Decision Diagrams (SDDs), for which a polytime Apply function exists for unreduced SDDs, but remains unknown for reduced ones (i.e. canonical SDDs). We resolve this open question in this paper and consider some of its theoretical and practical implications. Some of the findings we report question the common wisdom on the relationship between bottom-up compilation, language canonicity and the complexity of the Apply function
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