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59
Planning as satisfiability
 IN ECAI92
, 1992
"... We develop a formal model of planning based on satisfiability rather than deduction. The satis ability approach not only provides a more flexible framework for stating di erent kinds of constraints on plans, but also more accurately reflects the theory behind modern constraintbased planning systems ..."
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Cited by 506 (27 self)
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We develop a formal model of planning based on satisfiability rather than deduction. The satis ability approach not only provides a more flexible framework for stating di erent kinds of constraints on plans, but also more accurately reflects the theory behind modern constraintbased planning systems. Finally, we consider the computational characteristics of the resulting formulas, by solving them with two very different satisfiability testing procedures.
LPG: A planner based on local search for planning graphs
 In Proc. of 6th Int. Conf. on AI Planning Systems (AIPS’02
"... We present LPG, a fast planner using local search for solving planning graphs. LPG can use various heuristics based on a parametrized objective function. These parameters weight different types of inconsistencies in the partial plan represented by the current search state, and are dynamically evalua ..."
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Cited by 127 (6 self)
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We present LPG, a fast planner using local search for solving planning graphs. LPG can use various heuristics based on a parametrized objective function. These parameters weight different types of inconsistencies in the partial plan represented by the current search state, and are dynamically evaluated during search using Lagrange multipliers. LPG’s basic heuristic was inspired by Walksat, which in Kautz and Selman’s Blackbox can be used to solve the SATencoding of a planning graph. An advantage of LPG is that its heuristics exploit the structure of the planning graph, while Blackbox relies on general heuristics for SATproblems, and requires the translation of the planning graph into propositional clauses. Another major difference is that LPG can handle action costs to produce good quality plans. This is achieved by an “anytime ” process minimizing an objective function based on the number of inconsistencies in the partial plan and on its overall cost. The objective function can also take into account the number of parallel steps and the overall plan duration. Experimental results illustrate the efficiency of our approach showing, in particular, that for a set of wellknown benchmark domains LPG is significantly faster than existing Graphplanstyle planners.
TALplanner: A temporal logic based forward chaining planner
 ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
, 2001
"... We present TALplanner, a forwardchaining planner based on the use of domaindependent
search control knowledge represented as formulas in the Temporal Action
Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning
about action and change in incompletely speci#12;ed dynamic ..."
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Cited by 95 (17 self)
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We present TALplanner, a forwardchaining planner based on the use of domaindependent
search control knowledge represented as formulas in the Temporal Action
Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning
about action and change in incompletely speci#12;ed dynamic environments. TAL
is used as the formal semantic basis for TALplanner, where a TAL goal narrative
with control formulas is input to TALplanner which then generates a TAL narrative
that entails the goal and control formulas. The sequential version of TALplanner is
presented. The expressivity of plan operators is then extended to deal with an interesting
class of resource types. An algorithm for generating concurrent plans, where
operators have varying durations and internal state, is also presented. All versions
of TALplanner have been implemented. The potential of these techniques is demonstrated
by applying TALplanner to a number of standard planning benchmarks in
the literature.
Where Ignoring Delete Lists Works: Local Search Topology in Planning Benchmarks
, 2003
"... During the last five years, the planning community has seen vast progress in terms of the sizes of benchmark examples that domainindependent planners can tackle successfully. The key technique behind this progress is the use of heuristic functions based on relaxing the planning task at hand, where ..."
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Cited by 67 (12 self)
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During the last five years, the planning community has seen vast progress in terms of the sizes of benchmark examples that domainindependent planners can tackle successfully. The key technique behind this progress is the use of heuristic functions based on relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. The success of such methods in many of the current benchmarks suggests that in those task's state spaces relaxed goal distances yield a heuristic function of high quality.
How good is almost perfect
 In ICAPSWorkshop on Heuristics for DomainIndependent Planning
, 2007
"... Heuristic search using algorithms such as A ∗ and IDA ∗ is the prevalent method for obtaining optimal sequential solutions for classical planning tasks. Theoretical analyses of these classical search algorithms, such as the wellknown results of Pohl, Gaschnig and Pearl, suggest that such heuristic ..."
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Cited by 65 (4 self)
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Heuristic search using algorithms such as A ∗ and IDA ∗ is the prevalent method for obtaining optimal sequential solutions for classical planning tasks. Theoretical analyses of these classical search algorithms, such as the wellknown results of Pohl, Gaschnig and Pearl, suggest that such heuristic search algorithms can obtain better than exponential scaling behaviour, provided that the heuristics are accurate enough. Here, we show that for a number of common planning benchmark domains, including ones that admit optimal solution in polynomial time, general search algorithms such as A ∗ must necessarily explore an exponential number of search nodes even under the optimistic assumption of almost perfect heuristic estimators, whose heuristic error is bounded by a small additive constant. Our results shed some light on the comparatively bad performance of optimal heuristic search approaches in “simple” planning domains such as GRIPPER. They suggest that in many applications, further improvements in runtime require changes to other parts of the search algorithm than the heuristic estimator.
Automatic Synthesis and use of Generic Types in Planning
 In AIPS00
, 2000
"... This work is concerned with the automatic inference of generic types from STRIPS planning domain descriptions. Generic types are higher order types allowing the partition of domains (and components of domains) into different domain classes, including the commonly occurring transportation domain c ..."
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Cited by 58 (9 self)
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This work is concerned with the automatic inference of generic types from STRIPS planning domain descriptions. Generic types are higher order types allowing the partition of domains (and components of domains) into different domain classes, including the commonly occurring transportation domain class. We show how the generic type structure of domains can be exploited to increase planner efficiency. We have focussed so far on the generic types typical of transportation domains, but intend to go on to characterise, and identify examples of, other domain classes such as construction domains. One of the most interesting properties of the work described here is that domains which would not be recognised, by the human, as transportation domains can turn out to have an underlying transportation character which can be exploited by the application of heuristics suited to standard transportation domains. We illustrate this by considering both standard transportation domains (such as Logistics) and nonstandard ones (the PaintWall domain presented in this paper). The analyses described here are completely plannerindependent and contribute to an increasing collection of preplannin 9 analysis tools which help to increase performance of planners by decomposing and understanding the structures of planning problems before planners are applied.
Exhibiting Knowledge in Planning Problems to Minimize State Encoding Length
 In ECP
, 1999
"... In this paper we present a generalpurposed algorithm for transforming a planning problem specified in Strips into a concise state description for single state or symbolic exploration. The process of finding a state description consists of four phases. In the first phase we symbolically analyze the ..."
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Cited by 57 (19 self)
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In this paper we present a generalpurposed algorithm for transforming a planning problem specified in Strips into a concise state description for single state or symbolic exploration. The process of finding a state description consists of four phases. In the first phase we symbolically analyze the domain specification to determine constant and oneway predicates, i.e. predicates that remain unchanged by all operators or toggle in only one direction, respectively. In the second phase we symbolically merge predicates which lead to a drastic reduction of state encoding size, while in the third phase we constrain the domains of the predicates to be considered by enumerating the operators of the planning problem. The fourth phase combines the result of the previous phases.
Satisfiability Solvers
, 2008
"... The past few years have seen an enormous progress in the performance of Boolean satisfiability (SAT) solvers. Despite the worstcase exponential run time of all known algorithms, satisfiability solvers are increasingly leaving their mark as a generalpurpose tool in areas as diverse as software and h ..."
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Cited by 48 (0 self)
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The past few years have seen an enormous progress in the performance of Boolean satisfiability (SAT) solvers. Despite the worstcase exponential run time of all known algorithms, satisfiability solvers are increasingly leaving their mark as a generalpurpose tool in areas as diverse as software and hardware verification [29–31, 228], automatic test pattern generation [138, 221], planning [129, 197], scheduling [103], and even challenging problems from algebra [238]. Annual SAT competitions have led to the development of dozens of clever implementations of such solvers [e.g. 13,
Exploiting a graphplan framework in temporal planning
 in Int’l Conf. on Automated Planning and Scheduling, ICAPS 2003
, 2003
"... Graphplan (Blum & Furst 1995) has proved a popular and successful basis for a succession of extensions. An extension to handle temporal planning is a natural one to consider, because of the seductively timelike structure of the layers in the plan graph. TGP (Smith & Weld 1999) and TPSys (G ..."
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Cited by 38 (9 self)
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Graphplan (Blum & Furst 1995) has proved a popular and successful basis for a succession of extensions. An extension to handle temporal planning is a natural one to consider, because of the seductively timelike structure of the layers in the plan graph. TGP (Smith & Weld 1999) and TPSys (Garrido, Onaindı́a, & Barber 2001; Garrido, Fox, & Long 2002) are both examples of temporal planners that have exploited the Graphplan foundation. However, both of these systems (including both versions of TPSys) exploit the graph to represent a uniform flow of time. In this paper we describe an alternative approach, in which the graph is used to represent the purely logical structuring of the plan, with temporal constraints being managed separately (although not independently). The approach uses a linear constraint solver to ensure that temporal durations are correctly respected. The resulting planner offers an interesting alternative to the other approaches, offering an important extension in expressive power.
Hybrid STAN: Identifying and Managing Combinatorial Optimisation Subproblems in Planning
 In IJCAI01
, 2000
"... It is wellknown that planning is hard but it is less wellknown how to approach the hard parts of a problem instance eectively. Using static domain analysis techniques we can identify and abstract certain combinatorial subproblems from a planning instance, and deploy specialised technology to ..."
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Cited by 34 (5 self)
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It is wellknown that planning is hard but it is less wellknown how to approach the hard parts of a problem instance eectively. Using static domain analysis techniques we can identify and abstract certain combinatorial subproblems from a planning instance, and deploy specialised technology to solve these subproblems in a way that is integrated with the broader planning activities. We have developed a hybrid planning system (STAN4) which brings together alternative planning strategies and specialised algorithms and selects between them according to the structure of the planning domain. STAN4 participated successfully in the AIPS2000 planning competition. We describe how subproblem abstraction is done, with particular reference to routeplanning abstraction, and present some of the competition data to demonstrate the potential power of the hybrid approach. 1 Introduction The knowledgesparse, or domainindependent, planning community is often criticised for its o...