Planning Graph Heuristics for Belief Space Search

Bryce, Daniel; Kambhampati, Subbarao; Smith, David E.

doi:10.1613/jair.1869

Cited by 85 publications

(105 citation statements)

References 18 publications

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“…In our formulation, however, the definition of the progression function allows an action a to execute in any a-state σ if a is executable in σ, regardless whether or not a would add "new" information to σ. In contrast, our definition of the regression function requires that an action a can only be applied in a p-state (or a set of p-states) if a contributes something to the applied p-state(s) 5 . Thus, given a planning problem P = A, O, I, G , a progression solution c of P may contain redundant actions or extra branches.…”

Section: Completeness Resultmentioning

confidence: 99%

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A State-Based Regression Formulation for Domains with Sensing Actions and Incomplete Information

Tuan¹,

Baral²,

Son³

2006

Logical Methods in Computer Science

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Abstract. We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains with sensing actions to define the regression function. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one.

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Section: Completeness Resultmentioning

confidence: 99%

“…Example 1.1 indicates that this is inadequate for many planning problems. In the past, several planners capable of generating conditional plans have been developed (e.g., [5,11,21]) in which some form of the progression function has been used.…”

Section: State-based Regression In Incomplete Domainsmentioning

confidence: 99%

A State-Based Regression Formulation for Domains with Sensing Actions and Incomplete Information

Tuan¹,

Baral²,

Son³

2006

Logical Methods in Computer Science

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“…The OBDD formula was also used in the planner POND [Bryce et al, 2006] to represent literals and actions in the planning graph for computing heuristics. This BDD-based approach does not require any extra BDD manipulation operations and it can be applied in both directions: progression and regression.…”

Section: Motivation and Related Workmentioning

confidence: 99%

A generic approach to planning in the presence of incomplete information: Theory and implementation (Extended Abstract)

Son

Pontelli

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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This paper proposes a generic approach to planning in the presence of incomplete information. The approach builds on an abstract notion of a belief state representation, along with an associated set of basic operations. These operations facilitate the development of a sound and complete transition function, for reasoning about effects of actions in the presence of incomplete information, and a set of abstract algorithms for planning. The paper demonstrates how the abstract definitions and algorithms can be instantiated in three concrete representations: minimal-DNF, minimal-CNF, and prime implicates, resulting in three highly competitive conformant planners: DNF, CNF, and PIP. The paper includes an experimental evaluation of the planners DNF, CNF, and PIP and proposes a new set of conformant planning benchmarks that are challenging for state-of-the-art conformant planners. Motivation and Related WorkPlanning in the presence of incomplete information [Goldman and Boddy 1996;Smith and Weld, 1998] is the problem of generating a plan that can achieve a given goal regardless of what the actual truth value of unknown information about the initial world is. This paper focuses on conformant planning-a special class of planning problems in the presence of incomplete informationthat deals with incomplete information about the initial state and with deterministic and non-sensing actions.One of the challenges in conformant planning is the problem of reasoning about action effects in the presence of incomplete information. Specifically, consider an action a with a set of conditional effects of the form (a : ψ → η), where ψ and η are a set of literals. Each * This paper is based on the journal article [To, Son, and Pontelli, 2015]. effect (a : ψ → η) denotes that the set of literals η will be true in the resulting state after the execution of a in the current state if the e-condition ψ is true in the current state. Due to incomplete information, the current state and, hence, the truth value of ψ in it can be unknown. As a consequence, the fact that η must be true in the successor state can be unknown, making the computation of the successor state particularly challenging. This agrees with the results in [Baral et al., 2000;Haslum and Jonsson, 1999], that conformant planning for domains with conditional effects is at a higher complexity level than that without conditional effects. Since its introduction, conformant planning has attracted the attention of several researchers-leading to the development of several state-of-the-art conformant planners. It is important to observe that most efficient conformant planners are best-first search and progression-based planners, whose development starts with the selection of a representation language for incomplete (partially known) states and the definition of a transition (progression) function that, given a state S and an action, computes the next state S of the world, where S and S are generally incomplete and encoded in the representation.To deal with incomplete information a...

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“…Conformant planners such as Contingent-FF, MBP, and POND [24,25,26], address the search in belief space using suitable belief representations such as OBDDs, that do not necessarily blow up with the number of states deemed possible, and heuristics that can guide the search for the target beliefs. Another approach that has been pursued recently, that turned out to be the most competitive in the 2006 Int.…”

Section: Incomplete Informationmentioning

confidence: 99%

Inference and Learning in Planning

Geffner

2012

Inductive Logic Programming

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Abstract. Planning is concerned with the development of solvers for a wide range of models where actions must be selected for achieving goals. In these models, actions may be deterministic or not, and full or partial sensing may be available. In the last few years, significant progress has been made, resulting in algorithms that can produce plans effectively in a variety of settings. These developments have to do with the formulation and use of general inference techniques and transformations. In this invited talk, I'll review the inference techniques used for solving individual planning instances from scratch, and discuss the use of learning methods and transformations for obtaining more general solutions.

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Planning Graph Heuristics for Belief Space Search

Cited by 85 publications

References 18 publications

A State-Based Regression Formulation for Domains with Sensing Actions and Incomplete Information

A State-Based Regression Formulation for Domains with Sensing Actions and Incomplete Information

A generic approach to planning in the presence of incomplete information: Theory and implementation (Extended Abstract)

Inference and Learning in Planning

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