166 research outputs found
Plan permutation symmetries as a source of inefficiency in planning
This paper briefly reviews sources of symmetry in planning and highlights one source that has not previously been tackled: plan permutation symmetry. Symmetries can be a significant problem for efficiency of planning systems, as has been previously observed in the treatment of other forms of symmetry in planning problems. We examine how plan permutation symmetries can be eliminated and present evidence to support the claim that these symmetries are an important problem for planning systems
Progress in AI Planning Research and Applications
Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning
Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan
This paper reviews the connections between Graphplan's planning-graph and the
dynamic constraint satisfaction problem and motivates the need for adapting CSP
search techniques to the Graphplan algorithm. It then describes how explanation
based learning, dependency directed backtracking, dynamic variable ordering,
forward checking, sticky values and random-restart search strategies can be
adapted to Graphplan. Empirical results are provided to demonstrate that these
augmentations improve Graphplan's performance significantly (up to 1000x
speedups) on several benchmark problems. Special attention is paid to the
explanation-based learning and dependency directed backtracking techniques as
they are empirically found to be most useful in improving the performance of
Graphplan
The FF Planning System: Fast Plan Generation Through Heuristic Search
We describe and evaluate the algorithmic techniques that are used in the FF
planning system. Like the HSP system, FF relies on forward state space search,
using a heuristic that estimates goal distances by ignoring delete lists.
Unlike HSP's heuristic, our method does not assume facts to be independent. We
introduce a novel search strategy that combines hill-climbing with systematic
search, and we show how other powerful heuristic information can be extracted
and used to prune the search space. FF was the most successful automatic
planner at the recent AIPS-2000 planning competition. We review the results of
the competition, give data for other benchmark domains, and investigate the
reasons for the runtime performance of FF compared to HSP
Contingent planning under uncertainty via stochastic satisfiability
We describe a new planning technique that efficiently solves probabilistic propositional contingent planning problems by converting them into instances of stochastic satisfiability (SSAT) and solving these problems instead. We make fundamental contributions in two areas: the solution of SSAT problems and the solution of stochastic planning problems. This is the first work extending the planning-as-satisfiability paradigm to stochastic domains. Our planner, ZANDER, can solve arbitrary, goal-oriented, finite-horizon partially observable Markov decision processes (POMDPs). An empirical study comparing ZANDER to seven other leading planners shows that its performance is competitive on a range of problems. © 2003 Elsevier Science B.V. All rights reserved
Taming Numbers and Durations in the Model Checking Integrated Planning System
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
Distributed coordination in unstructured intelligent agent societies
Current research on multi-agent coordination and distributed problem
solving is still not robust or scalable enough to build large real-world
collaborative agent societies because it relies on either centralised components
with full knowledge of the domain or pre-defined social structures.
Our approach allows overcoming these limitations by using
a generic coordination framework for distributed problem solving on
totally unstructured environments that enables each agent to decompose
problems into sub-problems, identify those which it can solve
and search for other agents to delegate the sub-problems for which it
does not have the necessary knowledge or resources. Regarding the
problem decomposition process, we have developed two distributed
versions of the Graphplan planning algorithm. To allow an agent
to discover other agents with the necessary skills for dealing with
unsolved sub-problems, we have created two peer-to-peer search algorithms
that build and maintain a semantic overlay network that
connects agents relying on dependency relationships, which improves
future searches. Our approach was evaluated using two different scenarios,
which allowed us to conclude that it is efficient, scalable and
robust, allowing the coordinated distributed solving of complex problems
in unstructured environments without the unacceptable assumptions
of alternative approaches developed thus far.As abordagens actuais de coordenação multi-agente e resolução distribuída de problemas não são suficientemente robustas ou escaláveis
para criar sociedades de agentes colaborativos uma vez que assentam
ou em componentes centralizados com total conhecimento do
domínio ou em estruturas sociais pré-definidas. A nossa abordagem
permite superar estas limitações através da utilização de um algoritmo
genérico de coordenação de resolução distribuída de problemas
em ambientes totalmente não estruturados, o qual permite a cada
agente decompor problemas em sub-problemas, identificar aqueles que
consegue resolver e procurar outros agentes a quem delegar os subproblemas
para os quais não tem conhecimento suficiente. Para a
decomposição de problemas, criámos duas versões distribuídas do algoritmo
de planeamento Graphplan. Para procurar os agentes com as
capacidades necessárias à resolução das partes não resolvidas do problema,
criámos dois algoritmos de procura que constroem e mantêm
uma camada de rede semântica que relaciona agentes dependentes
com o fim de facilitar as procuras. A nossa abordagem foi avaliada
em dois cenários diferentes, o que nos permitiu concluir que ´e uma
abordagem eficiente, escalável e robusta, possibilitando a resolução
distribuída e coordenada de problemas complexos em ambientes não
estruturados sem os pressupostos inaceitáveis em que assentava o trabalho
feito até agora
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