Planning as heuristic search
Blai Bonet,Hector Geffner +1 more
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TLDR
A family of heuristic search planners are studied based on a simple and general heuristic that assumes that action preconditions are independent, which is used in the context of best-first and hill-climbing search algorithms, and tested over a large collection of domains.About:
This article is published in Artificial Intelligence.The article was published on 2001-06-01 and is currently open access. It has received 1023 citations till now. The article focuses on the topics: Incremental heuristic search & Beam search.read more
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Journal ArticleDOI
The FF planning system: fast plan generation through heuristic search
Jörg Hoffmann,Bernhard Nebel +1 more
TL;DR: A novel search strategy is introduced that combines hill-climbing with systematic search, and it is shown how other powerful heuristic information can be extracted and used to prune the search space.
Journal ArticleDOI
The fast downward planning system
TL;DR: Fast Downward as discussed by the authors uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators.
Proceedings Article
ARA*: Anytime A* with Provable Bounds on Sub-Optimality
TL;DR: An anytime heuristic search, ARA*, is proposed, which tunes its performance bound based on available search time, and starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows.
Journal ArticleDOI
The LAMA planner: guiding cost-based anytime planning with landmarks
Silvia Richter,Matthias Westphal +1 more
TL;DR: It is found that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial, and in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future.
Proceedings Article
Anytime dynamic A*: an anytime, replanning algorithm
TL;DR: A graph-based planning and replanning algorithm able to produce bounded suboptimal solutions in an anytime fashion that combines the benefits of anytime and incremental planners to provide efficient solutions to complex, dynamic search problems.
References
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Human Problem Solving
TL;DR: The aim of the book is to advance the understanding of how humans think by putting forth a theory of human problem solving, along with a body of empirical evidence that permits assessment of the theory.
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Network Flows: Theory, Algorithms, and Applications
TL;DR: In-depth, self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including descriptions of polynomial-time algorithms for these core models are presented.
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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.
Lecture Notes in Artificial Intelligence
P. Brezillon,P. Bouquet +1 more
TL;DR: The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.