Abstract
In this paper we combine the goal directed search of A* with the ability of BDDs to traverse an exponential number of states in polynomial time. We introduce a new algorithm, SetA*, that generalizes A* to expand sets of states in each iteration. SetA* has substantial advantages over BDDA*, the only previous BDD-based A* implementation we are aware of. Our experimental evaluation proves SetA* to be a powerful search paradigm. For some of the studied problems it outperforms BDDA*, A*, and BDD-based breadth-first search by several orders of magnitude. We believe exploring sets of states to be essential when the heuristic function is weak. For problems with strong heuristics, SetA* efficiently specializes to single-state search and consequently challenges single-state heuristic search in general.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Conference on Artificial Intelligence Planning Systems (AIPS-02) Workshop on Planning via Model Checking |
| Number of pages | 9 |
| Publisher | AAAI Press |
| Publication date | 2002 |
| Pages | 72-80 |
| Publication status | Published - 2002 |
Keywords
- Algorithmic Search
- Set Expansion
- Heuristic Function
- BDD-Based Methods
- State Space Traversal