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• W1598522239 abstract "Classical search methods on game trees are based on a static evaluation function that enable quantitative valuation of game states and a decision strategy such as the minimax method. These search methods are not always effective in some games such as the game of Go, as construction of the evaluation function is very hard and the search space is extremely huge. Recently, Monte Carlo tree search methods especially the UCT algorithms that enable efficient sampling of actions have been shown to be very effective. Here, we propose the loosely symmetric LS model applied to trees LST, which utilises an action value function LS model that implements causal intuition of humans. By tuning a single intuitive parameter, LST enables fast search of the optimal action with its efficient satisficing behaviour. The satisficing search realised by LST enables pruning and exhibits intermediate properties between those of breadth-first and depth-first search strategies." @default.
• W1598522239 created "2016-06-24" @default.
• W1598522239 creator A5010294722 @default.
• W1598522239 creator A5076653935 @default.
• W1598522239 date "2015-08-10" @default.
• W1598522239 modified "2023-09-24" @default.
• W1598522239 title "Efficacy of a causal value function in game tree search" @default.
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• W1598522239 doi "https://doi.org/10.1080/17445760.2015.1064918" @default.
• W1598522239 hasPublicationYear "2015" @default.
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