Planning Graph Heuristics for Belief Space Search

Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning

Efficient Open World Reasoning for Planning

We consider the problem of reasoning and planning with incomplete knowledge and deterministic actions. We introduce a knowledge representation scheme called PSIPLAN that can effectively represent incompleteness of an agent's knowledge while allowing for sound, complete and tractable entailment in domains where the set of all objects is either unknown or infinite. We present a procedure for state update resulting from taking an action in PSIPLAN that is correct, complete and has only polynomial complexity. State update is performed without considering the set of all possible worlds corresponding to the knowledge state. As a result, planning with PSIPLAN is done without direct manipulation of possible worlds. PSIPLAN representation underlies the PSIPOP planning algorithm that handles quantified goals with or without exceptions that no other domain independent planner has been shown to achieve. PSIPLAN has been implemented in Common Lisp and used in an application on planning in a collaborative interface.Comment: 39 pages, 13 figures. to appear in Logical Methods in Computer Scienc

Planning and learning under uncertainty

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

Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief

Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents

Deductive synthesis of recursive plans in linear logic

Centre for Intelligent Systems and their ApplicationsConventionally, the problem of plan formation in Artificial Intelligence deals with the generation of plans in the form of a sequence of actions. This thesis describes an approach to extending the expressiveness of plans to include conditional branches and recursion. This allows problems to be solved at a higher level, such that a single plan in such a language is capable of solving a class of problems rather than a single problem instance. A plan of fixed size may solve arbitrarily large problem instances. To form such plans, we take a deductive planning approach, in which the formation of the plan goes hand-in-hand with the construction of the proof that the plan specification is realisable. The formalism used here for specifying and reasoning with planning problems is Girard's Institutionistic Linear Logic (ILL), which is attractive for planning problems because state change can be expressed directly as linear implication, with no need for frame axioms. We extract plans by means of the relationship between proofs in ILL and programs in the style of Abramsky. We extend the ILL proof rules to account for induction over inductively defined types, thereby allowing recursive plans to be synthesised. We also adapt Abramsky's framework to partially evaluate and execute the plans in the extended language. We give a proof search algorithm tailored towards the fragment of the ILL employed (excluding induction rule selection). A system implementation, Lino, comprises modules for proof checking, automated proof search, plan extraction and partial evaluation of plans. We demonstrate the encodings and solutions in our framework of various planning domains involving recursion. We compare the capabilities of our approach with the previous approaches of Manna and Waldinger, Ghassem-Sani and Steel, and Stephen and Biundo. We claim that our approach gives a good balance between coverage of problems that can be described and the tractability of proof search

Hybrid conditional planning using answer set programming

We introduce a parallel offline algorithm for computing hybrid conditional plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies on modeling actuation actions and sensing actions in an expressive nonmonotonic language of answer set programming (ASP), and computation of the branches of a conditional plan in parallel using an ASP solver. In particular, thanks to external atoms, continuous feasibility checks (like collision checks) are embedded into formal representations of actuation actions and sensing actions in ASP; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing nonmonotonic constructs and nondeterministic choices, partial knowledge about states and nondeterministic effects of sensing actions can be explicitly formalized in ASP; and thus each branch of a conditional plan can be computed by an ASP solver without necessitating a conformant planner and an ordering of sensing actions in advance. We apply our method in a service robotics domain and report experimental evaluations. Furthermore, we present performance comparisons with other compilation based conditional planners on standardized benchmark domains