Solving the Entailment Problem in the Fluent Calculus Using Binary Decision Diagrams

Hölldobler, Steffen; Störr, Hans-Peter

doi:10.1007/3-540-44957-4_50

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Other examples of model checking based deterministic planners include, among others, ProPlan [26] and BDDPlan [34], while the Model Based Planner (MBP) [4] uses symbolic model checking for non-deterministic domains.…”

Section: Related Workmentioning

confidence: 99%

A universal planning system for hybrid domains

2011

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Many real world problems involve hybrid systems, subject to (continuous) physical effects and controlled by (discrete) digital equipments. Indeed, many efforts are being made to extend the current planning systems and modelling languages to support such kind of domains. However, hybrid systems often present also a nonlinear behaviour and planning with continuous nonlinear change that is still a challenging issue.In this paper we present the UPMurphi tool, a universal planner based on the discretise and validate approach that is capable of reasoning with mixed discrete/continuous domains, fully respecting the semantics of PDDL+. Given an initial discretisation, the hybrid system is discretised and given as input to UPMurphi, which performs universal planning on such an approximated model and checks the correctness of the results. If the validation fails, the approach is repeated by appropriately refining the discretisation.To show the effectiveness of our approach, the paper presents two real hybrid domains where universal planning has been successfully performed using the UPMurphi tool.

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Section: Related Workmentioning

confidence: 99%

A universal planning system for hybrid domains

2011

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“…The use of model checking approaches to solve planning problems has been explored in some depth, e.g. by Cimatti, Roveri, and Traverso (1998), Bertoli, Cimatti, Roveri, and Traverso (2001), Lago, Pistore, and Traverso (2002), Kvarnström, Doherty, and Haslum (2000), Bacchus and Kabanza (2000), Hölldobler and Stör (2000), Fourman (2000), Edelkamp (2003b), Dierks (2005), Kabanza and Thiébaux (2005). However, not much has been done in the inverse direction, applying planners to model checking problems.…”

Section: Motivationmentioning

confidence: 99%

Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4

Hoffmann¹,

Edelkamp²,

Thiébaux³

et al. 2006

jair

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In a field of research about general reasoning mechanisms, it is essential to have appropriate benchmarks. Ideally, the benchmarks should reflect possible applications of the developed technology. In AI Planning, researchers more and more tend to draw their testing examples from the benchmark collections used in the International Planning Competition (IPC). In the organization of (the deterministic part of) the fourth IPC, IPC-4, the authors therefore invested significant effort to create a useful set of benchmarks. They come from five different (potential) real-world applications of planning: airport ground traffic control, oil derivative transportation in pipeline networks, model-checking safety properties, power supply restoration, and UMTS call setup. Adapting and preparing such an application for use as a benchmark in the IPC involves, at the time, inevitable (often drastic) simplifications, as well as careful choice between, and engineering of, domain encodings. For the first time in the IPC, we used compilations to formulate complex domain features in simple languages such as STRIPS, rather than just dropping the more interesting problem constraints in the simpler language subsets. The article explains and discusses the five application domains and their adaptation to form the PDDL test suites used in IPC-4. We summarize known theoretical results on structural properties of the domains, regarding their computational complexity and provable properties of their topology under the h+ function (an idealized version of the relaxed plan heuristic). We present new (empirical) results illuminating properties such as the quality of the most wide-spread heuristic functions (planning graph, serial planning graph, and relaxed plan), the growth of propositional representations over instance size, and the number of actions available to achieve each fact; we discuss these data in conjunction with the best results achieved by the different kinds of planners participating in IPC-4.

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“…In the 2000 competition, two other symbolic planners took part: PropPlan (Fourman, 2000), and BDDPlan (Hölldobler & Stör, 2000). Although they did not receive any awards for performance, they show interesting properties.…”

Section: Model Checking Plannersmentioning

confidence: 99%

Taming Numbers and Durations in the Model Checking Integrated Planning System

Edelkamp¹

2003

jair

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The Model Checking Integrated Planning System (MIPS) is a temporal least commitment heuristic search planner based on a flexible object-oriented workbench architecture. Its design clearly separates explicit and symbolic directed exploration algorithms from the set of on-line and off-line computed estimates and associated data structures. MIPS has shown distinguished performance in the last two international planning competitions. In the last event the description language was extended from pure propositional planning to include numerical state variables, action durations, and plan quality objective functions. Plans were no longer sequences of actions but time-stamped schedules. As a participant of the fully automated track of the competition, MIPS has proven to be a general system; in each track and every benchmark domain it efficiently computed plans of remarkable quality. This article introduces and analyzes the most important algorithmic novelties that were necessary to tackle the new layers of expressiveness in the benchmark problems and to achieve a high level of performance. The extensions include critical path analysis of sequentially generated plans to generate corresponding optimal parallel plans. The linear time algorithm to compute the parallel plan bypasses known NP hardness results for partial ordering by scheduling plans with respect to the set of actions and the imposed precedence relations. The efficiency of this algorithm also allows us to improve the exploration guidance: for each encountered planning state the corresponding approximate sequential plan is scheduled. One major strength of MIPS is its static analysis phase that grounds and simplifies parameterized predicates, functions and operators, that infers knowledge to minimize the state description length, and that detects domain object symmetries. The latter aspect is analyzed in detail. MIPS has been developed to serve as a complete and optimal state space planner, with admissible estimates, exploration engines and branching cuts. In the competition version, however, certain performance compromises had to be made, including floating point arithmetic, weighted heuristic search exploration according to an inadmissible estimate and parameterized optimization.

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Solving the Entailment Problem in the Fluent Calculus Using Binary Decision Diagrams

Cited by 6 publications

References 11 publications

A universal planning system for hybrid domains

A universal planning system for hybrid domains

Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4

Taming Numbers and Durations in the Model Checking Integrated Planning System

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