Add to ORKGA promising approach to solving large state-space search problems is to integrate heuristic search with symbolic search. Recent work shows that a symbolic A * search al-gorithm that uses binary decision diagrams to compactly rep-resent sets of states outperforms traditional A * in many do-mains. Since the memory requirements of A * limit its scal-ability, we show how to integrate symbolic search with a memory-efficient strategy for heuristic search. We analyze the resulting search algorithm, consider the factors that affect its behavior, and evaluate its performance in solving bench-mark problems that include STRIPS planning problems
Optimal heuristic searches such as A * search are widely used for planning but can rarely scale to l...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
Heuristic search is a fundamental technique for solving problems in artificial intelligence. However...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive...
In this paper we propose refinements for optimal search with symbolic pattern databases in determini...
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deter...
A Reduced Ordered Binary Decision Diagram (BDD) is a symbolic data structure introduced to the model...
In this paper we study traditional and enhanced BDD-based exploration procedures capable of handling...
We describe a planning algorithm that integrates two approaches to solving Markov decision processe...
Symbolic search allows saving large amounts of memory compared to regular explicit-state search algo...
Our goal is to automatically generate heuristics to guide state space search. The heuristic values a...
Planning in nondeterministic domains has gained more and more importance. Conformant planning is the...
Search is an important topic in many areas of AI. Search problems often result in an immense number ...
This work combines recent advances in AI planning under memory limitation, namely bitvector and symb...
Optimal heuristic searches such as A * search are widely used for planning but can rarely scale to l...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
Heuristic search is a fundamental technique for solving problems in artificial intelligence. However...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive...
In this paper we propose refinements for optimal search with symbolic pattern databases in determini...
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deter...
A Reduced Ordered Binary Decision Diagram (BDD) is a symbolic data structure introduced to the model...
In this paper we study traditional and enhanced BDD-based exploration procedures capable of handling...
We describe a planning algorithm that integrates two approaches to solving Markov decision processe...
Symbolic search allows saving large amounts of memory compared to regular explicit-state search algo...
Our goal is to automatically generate heuristics to guide state space search. The heuristic values a...
Planning in nondeterministic domains has gained more and more importance. Conformant planning is the...
Search is an important topic in many areas of AI. Search problems often result in an immense number ...
This work combines recent advances in AI planning under memory limitation, namely bitvector and symb...
Optimal heuristic searches such as A * search are widely used for planning but can rarely scale to l...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
Heuristic search is a fundamental technique for solving problems in artificial intelligence. However...