Add to ORKGIn this paper we propose refinements for optimal search with symbolic pattern databases in deterministic state-space planning. As main memory is limited, external heuristic search is combined with the power of sym-bolic representation. We start with an external version of symbolic breadth-first search. Then an alternative and external implementation for BDDA * to include dif-ferent heuristic evaluation functions into the symbolic search process is presented. We evaluate the approach in benchmarks taken from the 4th international planning competition
Planning in nondeterministic domains has gained more and more importance. Conformant planning is the...
In this paper we illustrate efforts to perform memory efficient large-scale planning. We first gener...
In this article, we present a framework called state-set branching that combines symbolic search bas...
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...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
A Reduced Ordered Binary Decision Diagram (BDD) is a symbolic data structure introduced to the model...
Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive...
Symbolic search allows saving large amounts of memory compared to regular explicit-state search algo...
In this paper we study traditional and enhanced BDD-based exploration procedures capable of handling...
The heuristics used for planning and search often take the form of pattern databases generated from ...
We describe a planning algorithm that integrates two approaches to solving Markov decision processe...
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deter...
When planning in an uncertain environment, one is often interested in finding a contingent plan that...
Search is an important topic in many areas of AI. Search problems often result in an immense number ...
Planning in nondeterministic domains has gained more and more importance. Conformant planning is the...
In this paper we illustrate efforts to perform memory efficient large-scale planning. We first gener...
In this article, we present a framework called state-set branching that combines symbolic search bas...
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...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
A Reduced Ordered Binary Decision Diagram (BDD) is a symbolic data structure introduced to the model...
Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive...
Symbolic search allows saving large amounts of memory compared to regular explicit-state search algo...
In this paper we study traditional and enhanced BDD-based exploration procedures capable of handling...
The heuristics used for planning and search often take the form of pattern databases generated from ...
We describe a planning algorithm that integrates two approaches to solving Markov decision processe...
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deter...
When planning in an uncertain environment, one is often interested in finding a contingent plan that...
Search is an important topic in many areas of AI. Search problems often result in an immense number ...
Planning in nondeterministic domains has gained more and more importance. Conformant planning is the...
In this paper we illustrate efforts to perform memory efficient large-scale planning. We first gener...
In this article, we present a framework called state-set branching that combines symbolic search bas...