Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search
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TLDR
A novel approach for successfully harnessing the advantages of the two competing paradigms to develop planners that are significantly more powerful than either of the approaches, and presents AltAlt, a planner literally cobbled together from the implementations of Graphplan and state search style planners.About:
This article is published in Artificial Intelligence.The article was published on 2002-02-01 and is currently open access. It has received 111 citations till now. The article focuses on the topics: Graphplan & Heuristics.read more
Citations
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Journal ArticleDOI
Sapa: a multi-objective metric temporal planner
TL;DR: An implementation of SAPA using many of the techniques presented in this paper was one of the best domain independent planners for domains with metric and temporal constraints in the third International Planning Competition, held at AIPS-02.
Proceedings Article
When is temporal planning really temporal
TL;DR: A complete state-space temporal planning algorithm is designed, which the authors hope will be able to achieve high performance by leveraging the heuristics that power decision epoch planners.
Proceedings Article
Reviving partial order planning
TL;DR: This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitive with the state of the art plan synthesis algorithms.
Journal ArticleDOI
Planning graph heuristics for belief space search
TL;DR: A formal basis for distance estimates between belief states is provided and a definition for the distance between beliefStates that relies on aggregating underlying state distance measures is given.
Proceedings Article
A lookahead strategy for heuristic search planning
TL;DR: This work presents a novel way for extracting information from the relaxed plan and for dealing with helpful actions, by considering the high quality of the relaxed plans in numerous domains, in a complete best-first search algorithm.
References
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Book
Principles of Artificial Intelligence
TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.
Proceedings Article
Fast planning through planning graph analysis
Avrim Blum,Merrick L. Furst +1 more
TL;DR: A new approach to planning in STRIPS-like domains based on constructing and analyzing a compact structure the authors call a Planning Graph is introduced, and a new planner, Graphplan, is described that uses this paradigm.
Book
Foundations of Constraint Satisfaction
TL;DR: Introduction to the C SP CSP solving - an overview chapter fundamental concepts of the CSP chapter problem reduction chapter basic search strategies for solving CSPs search orders in searching in C SPs exploitation of problem specific features stochastic search methods.
Book
STRIPS: a new approach to the application of theorem proving to problem solving
Richard Fikes,Nils J. Nilsson +1 more
TL;DR: In this article, the authors describe a problem solver called STRIPS that attempts to find a sequence of operators in a spcce of world models to transform a given initial world model into a model in which a given goal formula can be proven to be true.
Journal ArticleDOI
Fast planning through planning graph analysis
Avrim Blum,Merrick L. Furst +1 more
TL;DR: Graphplan as mentioned in this paper is a partial-order planner based on constructing and analyzing a compact structure called a planning graph, which can be used to find the shortest possible partial order plan or state that no valid plan exists.
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