An examination of brute force intelligence
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
On the branching factor of the alpha-beta pruning algorithm
Artificial Intelligence
Contingent planning under uncertainty via stochastic satisfiability
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
PickPocket: A computer billiards shark
Artificial Intelligence
From State-of-the-Art Static Fleet Assignment to Flexible Stochastic Planning of the Future
Algorithmics of Large and Complex Networks
CHANCEPROBCUT: forward pruning in chance nodes
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Total-order multi-agent task-network planning for contract bridge
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Artificial Intelligence
Exploiting the rule structure for decision making within the independent choice logic
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Integrating planning and execution in stochastic domains
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Parallelism for perturbation management and robust plans
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
Rediscovering *-MINIMAX search
CG'04 Proceedings of the 4th international conference on Computers and Games
*-MINIMAX performance in backgammon
CG'04 Proceedings of the 4th international conference on Computers and Games
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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An extention of the alpha-beta tree pruning strategy to game trees with 'probability' nodes, whose values are defined as the (possibly weighted) average of their successors' values, is developed. These '*-minimax' trees pertain to games involving chance but no concealed information. Based upon our search strategy, we formulate and then analyze several algorithms for *-minimax trees. An initial left-to-right depth-first algorithm is developed and shown to reduce the complexity of an exhaustive search strategy by 25-30 percent. An improved algorithm is then formulated to 'probe' beneath the chance nodes of 'regular' *-minimax trees, where players alternate in making moves with chance events interspersed. With random ordering of successor nodes, this modified algorithm is shown to reduce search by more than 50 percent. With optimal ordering, it is shown to reduce search complexity by an order of magnitude. After examining the savings of the first two algorithms on deeper trees, two additional algorithms are presented and analyzed.