Add to ORKGSymbolic search using BDDs usually saves huge amounts of memory, while in some domains its savings are moderate at best. It is an open problem to determine if BDDs work well for a certain domain. Motivated by finding evidences for BDD growths for state space search, in this paper we are concerned with symbolic search in the domain of Connect Four. We prove that there is a variable ordering for which the set of all possible states – when continuing after a terminal state has been reached – can be represented by polynomial sized BDDs, whereas the termination criterion leads to an exponential number of nodes in the BDD given any variable ordering
A promising approach to solving large state-space search problems is to integrate heuristic search w...
Verification techniques using symbolic state space traversal rely on efficient algorithms based on B...
We establish $O(n \log n)$ minimum-space algorithms that omit both the open and the closed list to d...
The idea of using BDDs for optimal sequential planning is to reduce the memory requirements for the ...
In this article, we present a framework called state-set branching that combines symbolic search bas...
A Reduced Ordered Binary Decision Diagram (BDD) is a symbolic data structure introduced to the model...
This work combines recent advances in AI planning under memory limitation, namely bitvector and symb...
Symbolic search allows saving large amounts of memory compared to regular explicit-state search algo...
In this paper we combine the goal directed search of A* with the ability of BDDs to traverse an exp...
Symbolic search with BDDs has shown remarkable performance for cost-optimal deterministic planning b...
In this paper we combine the goal directed search of A * with the ability of BDDs to traverse an exp...
AbstractIn this article, we present a framework called state-set branching that combines symbolic se...
Search is an important topic in many areas of AI. Search problems often result in an immense number ...
State-space exploration is an essential step in many modeling and analysis problems. Its goal is to ...
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...
Verification techniques using symbolic state space traversal rely on efficient algorithms based on B...
We establish $O(n \log n)$ minimum-space algorithms that omit both the open and the closed list to d...
The idea of using BDDs for optimal sequential planning is to reduce the memory requirements for the ...
In this article, we present a framework called state-set branching that combines symbolic search bas...
A Reduced Ordered Binary Decision Diagram (BDD) is a symbolic data structure introduced to the model...
This work combines recent advances in AI planning under memory limitation, namely bitvector and symb...
Symbolic search allows saving large amounts of memory compared to regular explicit-state search algo...
In this paper we combine the goal directed search of A* with the ability of BDDs to traverse an exp...
Symbolic search with BDDs has shown remarkable performance for cost-optimal deterministic planning b...
In this paper we combine the goal directed search of A * with the ability of BDDs to traverse an exp...
AbstractIn this article, we present a framework called state-set branching that combines symbolic se...
Search is an important topic in many areas of AI. Search problems often result in an immense number ...
State-space exploration is an essential step in many modeling and analysis problems. Its goal is to ...
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...
Verification techniques using symbolic state space traversal rely on efficient algorithms based on B...
We establish $O(n \log n)$ minimum-space algorithms that omit both the open and the closed list to d...