Results 1  10
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105
The Computational Complexity of Propositional STRIPS Planning
 Artificial Intelligence
, 1994
"... I present several computational complexity results for propositional STRIPS planning, i.e., STRIPS planning restricted to ground formulas. Different planning problems can be defined by restricting the type of formulas, placing limits on the number of pre and postconditions, by restricting negation ..."
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Cited by 293 (3 self)
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I present several computational complexity results for propositional STRIPS planning, i.e., STRIPS planning restricted to ground formulas. Different planning problems can be defined by restricting the type of formulas, placing limits on the number of pre and postconditions, by restricting negation in pre and postconditions, and by requiring optimal plans. For these types of restrictions, I show when planning is tractable (polynomial) and intractable (NPhard) . In general, it is PSPACEcomplete to determine if a given planning instance has any solutions. Extremely severe restrictions on both the operators and the formulas are required to guarantee polynomial time or even NPcompleteness. For example, when only ground literals are permitted, determining plan existence is PSPACEcomplete even if operators are limited to two preconditions and two postconditions. When definite Horn ground formulas are permitted, determining plan existence is PSPACEcomplete even if operators are limited t...
HTN planning: Complexity and expressivity
 In AAAI94
, 1994
"... Most practical work on AI planning systems during the last fteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies with var ..."
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Cited by 250 (17 self)
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Most practical work on AI planning systems during the last fteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies with various conditions on the task networks.
Bridging the gap between planning and scheduling
 Knowledge Engineering Review
"... Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting orde ..."
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Cited by 94 (9 self)
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Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of AI planning and scheduling techniques, focusing on their similarities, differences, and limitations. We also argue that many difficult practical problems lie somewhere between planning and scheduling, and that neither area has the right set of tools for solving these vexing problems. 1 The Ambitious Spacecraft Imagine a hypothetical spacecraft enroute to a distant planet. Between propulsion cycles, there are time windows when the craft can be turned for communication and scientific observations. At any given time, the spacecraft has a large set of possible scientific observations that it can perform, each having some value or priority. For each observation, the spacecraft will need to be turned towards the target and the required measurement or exposure taken. Unfortunately, turning to a target is a slow operation that may take up to 30 minutes, depending on the magnitude of the turn. As a result, the choice of experiments and the order in which they are performed has a significant impact on the duration of turns and, therefore, on how much can be accomplished. All this is further complicated by several things:
On the Complexity of BlocksWorld Planning
 Artificial Intelligence
, 1992
"... In this paper, we show that in the bestknown version of the blocks world (and several related versions), planning is difficult, in the sense that finding an optimal plan is NPhard. However, the NPhardness is not due to deletedcondition interactions, but instead due to a situation which we call a ..."
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Cited by 79 (15 self)
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In this paper, we show that in the bestknown version of the blocks world (and several related versions), planning is difficult, in the sense that finding an optimal plan is NPhard. However, the NPhardness is not due to deletedcondition interactions, but instead due to a situation which we call a deadlock. For problems that do not contain deadlocks, there is a simple hillclimbing strategy that can easily find an optimal plan, regardless of whether or not the problem contains any deletedcondition interactions. The above result is rather surprising, since one of the primary roles of the blocks world in the planning literature has been to provide examples of deletedcondition interactions such as creative destruction and Sussman's anomaly. However, we can explain why deadlocks are hard to handle in terms of a domainindependent goal interaction which we call an enablingcondition interaction, in which an action invoked to achieve one goal has a sideeffect of making it easier to achi...
The Computational Complexity of Probabilistic Planning
 Journal of Artificial Intelligence Research
, 1998
"... We examine the computational complexity of testing and finding small plans in probabilistic planning domains with both flat and propositional representations. The complexity of plan evaluation and existence varies with the plan type sought; we examine totally ordered plans, acyclic plans, and loopin ..."
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Cited by 77 (5 self)
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We examine the computational complexity of testing and finding small plans in probabilistic planning domains with both flat and propositional representations. The complexity of plan evaluation and existence varies with the plan type sought; we examine totally ordered plans, acyclic plans, and looping plans, and partially ordered plans under three natural definitions of plan value. We show that problems of interest are complete for a variety of complexity classes: PL, P, NP, coNP, PP, NP PP, coNP PP , and PSPACE. In the process of proving that certain planning problems are complete for NP PP , we introduce a new basic NP PP complete problem, EMajsat, which generalizes the standard Boolean satisfiability problem to computations involving probabilistic quantities; our results suggest that the development of good heuristics for EMajsat could be important for the creation of efficient algorithms for a wide variety of problems.
Using RegressionMatch Graphs to Control Search in Planning
 Artificial Intelligence
, 1999
"... Classical planning is the problem of finding a sequence of actions to achieve a goal given an exact characterization of a domain. An algorithm to solve this problem is presented, which searches a space of plan prefixes, trying to extend one of them to a complete sequence of actions. It is guided by ..."
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Cited by 63 (2 self)
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Classical planning is the problem of finding a sequence of actions to achieve a goal given an exact characterization of a domain. An algorithm to solve this problem is presented, which searches a space of plan prefixes, trying to extend one of them to a complete sequence of actions. It is guided by a heuristic estimator based on regressionmatch graphs, which attempt to characterize the entire subgoal structure of the remaining part of the problem. These graphs simplify the structure by neglecting goal interactions and by assuming that variables in goal conjunctions should be bound in such a way as to make as many conjuncts as possible true without further work. In some domains, these approximations work very well, and experiments show that many classicalplanning problems can solved with very little search. 1 Definition of the Problem The classical planning problem is to generate a sequence of actions that make a given proposition true, in a domain in which there is perfect informati...
Computational Complexity of Planning and Approximate Planning in the Presence of Incompleteness
, 1999
"... In the last several years, there have been several studies about the computational complexity of classical planning assuming that the planner has complete knowledge about the initial situation. Recently, there have been proposals to use `sensing' actions to plan in the presence of incompleteness. ..."
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Cited by 57 (8 self)
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In the last several years, there have been several studies about the computational complexity of classical planning assuming that the planner has complete knowledge about the initial situation. Recently, there have been proposals to use `sensing' actions to plan in the presence of incompleteness. In this paper we study the complexity of planning in such cases. In our study we use the action description language A proposed in 1991 by Gelfond and Lifschitz, and its extensions. It is known that if we consider only plans of tractable (polynomial) duration, planning in A  with complete information about the initial situation  is NPcomplete: even checking whether a given objective is attainable from a given initial state is NPcomplete. In this paper, we show that the planning problem in the presence of incompleteness is indeed harder: it belongs to the next level of the complexity hierarchy (in precise terms, it is \Sigma 2 Pcomplete). To overcome the complexity of this pro...
Planning by Rewriting: Efficiently Generating HighQuality Plans
 In Proceedings of the Fourteenth National Conference on Artificial Intelligence
, 1997
"... Domainindependent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. We introduce a new paradigm for efficient highquality planning that exploits plan rewriting rules and efficient local search techniques to transform an easytogenerate, ..."
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Cited by 50 (12 self)
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Domainindependent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. We introduce a new paradigm for efficient highquality planning that exploits plan rewriting rules and efficient local search techniques to transform an easytogenerate, but possibly suboptimal, initial plan into a lowcost plan. In addition to addressing the issues of efficiency and quality, this framework yields a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The results show that this approach provides significant savings in planning effort while generating highquality plans. Introduction Planning is the process of generating a network of actions that achieves a desired goal from an initial state of the world. Domain independent planning accepts as input, not only the initial state and the goal, but also the domain specification (i.e., the operators). This is a problem of considerable prac...
On the complexity of domainindependent planning
 In Proc. AAAI92. 381–386
, 1992
"... In this paper, we examine how the complexity of domainindependent planning with stripsstyle operators depends on the nature of the planning operators. We show how the time complexity varies depending on a wide variety of conditions: • whether or not delete lists are allowed; • whether or not negat ..."
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Cited by 49 (7 self)
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In this paper, we examine how the complexity of domainindependent planning with stripsstyle operators depends on the nature of the planning operators. We show how the time complexity varies depending on a wide variety of conditions: • whether or not delete lists are allowed; • whether or not negative preconditions are allowed; • whether or not the predicates are restricted to be propositions (i.e., 0ary); • whether the planning operators are given as part of the input to the planning problem, or instead are fixed in advance.
Relating Stable Models and AI Planning Domains
 In Proc. ICLP95
, 1995
"... In this paper, we show that there is a simple connection between logic programming and planning. The main result of this paper is the following: given any planning domain consisting of an initial state, and a set of operation definitions, this domain can be translated, in lineartime, to a logic pro ..."
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Cited by 47 (1 self)
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In this paper, we show that there is a simple connection between logic programming and planning. The main result of this paper is the following: given any planning domain consisting of an initial state, and a set of operation definitions, this domain can be translated, in lineartime, to a logic program such that a given goal G is achievable in the planning domain iff a related goal G ? is true in some stable model of the logic program obtained by the translation. We show that this translation yields at least two interesting consequences: (1) methods to update databases can be used to handle surprises when executing plans (i.e. a surprise occurs when an initial plan is partly executed, but one of the resulting intermediate states differs, perhaps due to external reasons, from what is predicted). (2) rigid actions, which are actions that must be executed when their preconditions are true, can be easily accommodated within our framework as well. 1 Introduction One of the fundamental ...