In this paper we study traditional and enhanced BDD-based exploration procedures capable of handling large planning problems. On the one hand, reachability analysis and model checking have eventually approached AI-Planning. Unfortu-nately, they typically rely on uninformed blind search. On the other hand, heuristic search and especially lower bound techniques have matured in effectively directing the explo-ration even for large problem spaces. Therefore, with heuris-tic symbolic search we address the unexplored middle ground between single state and symbolic planning engines to estab-lish algorithms that can gain from both sides. To this end we implement and evaluate heuristics found in state-of-the-art heuristic single-state search planner...
We describe a planning algorithm that integrates two approaches to solving Markov decision processes...
This thesis explores limitations of heuristic search planning, and presents techniques to overcome t...
Several real world applications require planners that deal with non-deterministic domains and with t...
Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive...
We describe a planning algorithm that integrates two approaches to solving Markov decision processe...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deter...
Search is an important topic in many areas of AI. Search problems often result in an immense number ...
Symbolic search with BDDs has shown remarkable performance for cost-optimal deterministic planning b...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
Heuristic search is a successful approach to cost-optimal planning. Bidirectional heuristic search a...
Planning in nondeterministic domains has gained more and more importance. Conformant planning is the...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
In this paper we propose refinements for optimal search with symbolic pattern databases in determini...
The objective of optimal oversubscription planning is to find a plan that yields an end state with a...
We describe a planning algorithm that integrates two approaches to solving Markov decision processes...
This thesis explores limitations of heuristic search planning, and presents techniques to overcome t...
Several real world applications require planners that deal with non-deterministic domains and with t...
Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive...
We describe a planning algorithm that integrates two approaches to solving Markov decision processe...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deter...
Search is an important topic in many areas of AI. Search problems often result in an immense number ...
Symbolic search with BDDs has shown remarkable performance for cost-optimal deterministic planning b...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
Heuristic search is a successful approach to cost-optimal planning. Bidirectional heuristic search a...
Planning in nondeterministic domains has gained more and more importance. Conformant planning is the...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
In this paper we propose refinements for optimal search with symbolic pattern databases in determini...
The objective of optimal oversubscription planning is to find a plan that yields an end state with a...
We describe a planning algorithm that integrates two approaches to solving Markov decision processes...
This thesis explores limitations of heuristic search planning, and presents techniques to overcome t...
Several real world applications require planners that deal with non-deterministic domains and with t...