Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Experiments in search and knowledge
Experiments in search and knowledge
Motion planning in the presence of movable obstacles
SCG '88 Proceedings of the fourth annual symposium on Computational geometry
Essentials of artificial intelligence
Essentials of artificial intelligence
Single-agent search in the presence of deadlocks
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Sokoban: Evaluating Standard Single-Agent Search Techniques in the Presence of Deadlock
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Finding optimal solutions to Rubik's cube using pattern databases
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Hi-index | 0.00 |
Humans can effectively navigate through large search spaces, enabling them to solve problems with daunting complexity. This is largely due to an ability to successfully distinguish between relevant and irrelevant actions (moves). In this paper we present a new single-agent search pruning technique that is based on a move's influence. The influence measure is a crude form of relevance in that it is used to differentiate between local (relevant) moves and non-local (not relevant) moves, with respect to the sequence of moves leading up to the current state. Our pruning technique uses the m previous moves to decide if a move is relevant in the current context and, if not, to cut it off. This technique results in a large reduction in the search effort required to solve Sokoban problems.