Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Abstraction Heuristics for Symbolic Bidirectional Search / 3272
álvaro Torralba, Carlos Linares López, Daniel Borrajo

Symbolic bidirectional uniform-cost search is a prominent technique for cost-optimal planning. Thus, the question whether it can be further improved by making use of heuristic functions raises naturally. However, the use of heuristics in bidirectional search does not always improve its performance. We propose a novel way to use abstraction heuristics in symbolic bidirectional search in which the search only resorts to heuristics when it becomes unfeasible. We adapt the definition of partial and perimeter abstractions to bidirectional search, where A* is used to traverse the abstract state spaces and/or generate the perimeter. The results show that abstraction heuristics can further improve symbolic bidirectional search in some domains. In fact, the resulting planner, SymBA*, was the winner of the optimal-track of the last IPC.