Some Results on the Complexity of Planning with Incomplete Information

Haslum, Patrik; Jönsson, Peter

doi:10.1007/10720246_24

Cited by 39 publications

(35 citation statements)

References 4 publications

(4 reference statements)

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“…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.…”

Section: Motivation and Related Worksupporting

confidence: 92%

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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Section: Motivation and Related Worksupporting

confidence: 92%

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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“…Thus, while in classical planning, the size of the (state) space to search is exponential in the number of variables in the problem; in conformant planning, the size of the (belief) space to search is exponential in the number of states. Indeed, conformant planning is harder than classical planning, as even the verification of conformant plans is NP-hard [23].…”

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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“…This is one of the most general problems considered in the area of planning and one of the hardest [3,4]. In the last few years, significant progress has been achieved resulting in a variety of contingent planners that can solve large and non-trivial problems, usually by casting the contingent planning problem as an AND/OR search over belief space [5] guided by effective heuristics and belief representations [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Contingent plans of exponential size follow naturally from situations where the number of observations that needs to be done is linear in the size of the problem. 3 The goal of this work is to use domain-independent planning techniques for dealing with such problems. However, rather than aiming at constructing full contingent plans, we aim at an effective action selection mechanism that chooses the action to do next in a closed-loop fashion.…”

Section: Introductionmentioning

confidence: 99%

Fast and Informed Action Selection for Planning with Sensing

Albore

Palacios

Geffner

2007

Current Topics in Artificial Intelligence

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Abstract. Consider a robot whose task is to pick up some colored balls from a grid, taking the red balls to a red spot, the blue balls to a blue spot and so on, one by one, without knowing either the location or color of the balls but having a sensor that can find out both when a ball is near. This problem is simple and can be solved by a domain-independent contingent planner in principle, but in practice this is not possible: the size of any valid plan constructed by a contingent planner is exponential in the number of observations which in these problems is very large. This doesn't mean that planning techniques are of no use for these problems but that building or verifying complete contingent plans is not feasible in general. In this work, we develop a domain-independent action selection mechanism that does not build full contingent plans but just chooses the action to do next in a closed-loop fashion. For this to work, however, the mechanism must be both fast and informed. We take advantage of recent ideas that allow delete and precondition-free contingent problems to be converted into conformant problems, and conformant problems into classical ones, for mapping the action selection problem in contingent planning into an action selection problem in classical planning that takes sensing actions into account. The formulation is tested over standard contingent planning benchmarks and problems that require plans of exponential size.

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Some Results on the Complexity of Planning with Incomplete Information

Cited by 39 publications

References 4 publications

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

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

Inference and Learning in Planning

Fast and Informed Action Selection for Planning with Sensing

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