On the complexity of blocks-world planning

Gupta, Naresh; Nau, Dana S.

doi:10.1016/0004-3702(92)90028-v

Cited by 170 publications

(91 citation statements)

References 10 publications

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“…Such policies are not optimal but are general, can be applied efficiently (they involve no search), and often produce good results. For example, a generalized policy for the blocks world can be given as follows (see [14,15] for better policies for this domain):…”

Section: Policiesmentioning

confidence: 99%

“…The strategy is completely domain-dependent (it only applies to the blocks-world), but is completely instanceindependent (it applies to all the domain instances). Moreover, the length of the plans it produces is never more than twice the length of the optimal plans [14]. Similar policies can be defined for many planning domains; e.g., [5] formulates a general policy for logistics problems.…”

Section: Policiesmentioning

confidence: 99%

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Learning Generalized Policies from Planning Examples Using Concept Languages

Martín

Geffner

2004

Applied Intelligence

103

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Abstract. In this paper we are concerned with the problem of learning how to solve planning problems in one domain given a number of solved instances. This problem is formulated as the problem of inferring a function that operates over all instances in the domain and maps states and goals into actions. We call such functions generalized policies and the question that we address is how to learn suitable representations of generalized policies from data. This question has been addressed recently by Roni Khardon (Technical Report TR-09-97, Harvard, 1997). Khardon represents generalized policies using an ordered list of existentially quantified rules that are inferred from a training set using a version of Rivest's learning algorithm (Machine Learning, vol. 2, no. 3, pp. 229-246, 1987). Here, we follow Khardon's approach but represent generalized policies in a different way using a concept language. We show through a number of experiments in the blocks-world that the concept language yields a better policy using a smaller set of examples and no background knowledge.

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Section: Policiesmentioning

confidence: 99%

Section: Policiesmentioning

confidence: 99%

Learning Generalized Policies from Planning Examples Using Concept Languages

Martín

Geffner

2004

Applied Intelligence

103

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“…The primitive actions consisted of four operators; pickup, putdown, stack and unstack. Due to space constraints, refer to [9] for full details. An axiom was used to indicate whether a block needed to be moved.…”

Section: Similarities and Differences Between Htn And Bdimentioning

confidence: 99%

“…The JSHOP blocks-world domain representation as well as sample problems from 10 blocks to 100 blocks was provided with the JSHOP planner (originally obtained from [9]). We used the problems provided and created additional problems for 110, 120, 130, 140 and 150 blocks by combining the 100 blocks problem with 10 blocks problems (including block renumbering).…”

Section: Similarities and Differences Between Htn And Bdimentioning

confidence: 99%

A Comparison of BDI Based Real-Time Reasoning and HTN Based Planning

Silva

Padgham

2004

Lecture Notes in Computer Science

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Abstract. The Belief-Desire-Intention (BDI) model of agency is an architecture based on Bratman's theory of practical reasoning. Hierarchical Task Network (HTN) decomposition on the other hand is a planning technique which has its roots in classical planning systems such as STRIPS. Despite being used for different purposes, HTN and BDI systems appear to have a lot of similarities in the problem solving approaches they adopt. This paper presents these similarities. A systematic method for mapping between the two systems is developed, and experimental results for different kinds of environments are presented.

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“…This version of Blocks world is known as the Elementary Blocks World (EBW). There are also other variations of this problem [38].…”

Section: Example: Blocks Worldmentioning

confidence: 99%

Computational Complexity of some Optimization Problems in Planning

Aghighi¹

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Automated planning is known to be computationally hard in the general case. Propositional planning is PSPACE-complete and first-order planning is undecidable. One method for analyzing the computational complexity of planning is to study restricted subsets of planning instances, with the aim of differentiating instances with varying complexity. We use this methodology for studying the computational complexity of planning.Finding new tractable (i.e. polynomial-time solvable) problems has been a particularly important goal for researchers in the area. The reason behind this is not only to differentiate between easy and hard planning instances, but also to use polynomial-time solvable instances in order to construct better heuristic functions and improve planners.We identify a new class of tractable cost-optimal planning instances by restricting the causal graph. We also study the computational complexity of oversubscription planning (such as the net-benefit problem) under various restrictions and reveal strong connections with classical planning. Inspired by this, we present a method for compiling oversubscription planning problems into the ordinary plan existence problem. We further study the parameterized complexity of cost-optimal and net-benefit planning under the same restrictions and show that the choice of numeric domain for the action costs has a great impact on the parameterized complexity.We finally consider the parameterized complexity of certain problems related to partial-order planning. In some applications, less restricted plans than total-order plans are needed. Therefore, a partial-order plan is being used instead. When dealing with partial-order plans, one important question is how to achieve optimal partial order plans, i.e. having the highest degree of freedom according to some notion of flexibility. We study several optimization problems for partial-order plans, such as finding a minimum deordering or reordering, and finding the minimum parallel execution length.The research presented in this thesis has been partially funded by the National Graduate School of Computer Science in Sweden (CUGS).iii Populärvetenskaplig sammanfattningHuvudtemat i denna avhandling är studiet av beräkningskomplexitet för automatisk planering. På en hög nivå kan man betrakta planeringsproblemet så här; man utgår från en värld eller ett system, som ett obemannat fordon eller en autonom robot, och antar att det finns vissa handlingar som ändrar tillståndet i systemet. Man antar dessutom att det finns ett initial-och ett måltillstånd. Planering är problemet att hitta en sekvens av handlingar som tranformerar initialtillståndet till måltillståndet. En sådan sekvens av handlingar kallas en plan. En planerare är ett datorprogram som löser planeringsproblem, och planerare baseras ofta på sökning genom tillstånden i systemet. Vanligtvis vill man optimera sökningen och hitta en plan som är optimal med avseende på något kriterium. Vad man vill optimera beror på tillämpningen men det är ofta att hitta en kortaste eller b...

show abstract

On the complexity of blocks-world planning

Cited by 170 publications

References 10 publications

Learning Generalized Policies from Planning Examples Using Concept Languages

Learning Generalized Policies from Planning Examples Using Concept Languages

A Comparison of BDI Based Real-Time Reasoning and HTN Based Planning

Computational Complexity of some Optimization Problems in Planning

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