Best-first minimax search

Korf, Richard E.; Chickering, David Maxwell

doi:10.1016/0004-3702(95)00096-8

Cited by 44 publications

(5 citation statements)

References 20 publications

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“…We now present the framework of the Descent algorithm (Cohen-Solal, 2020). The learning framework of Descent is based on Unbounded Minimax (Korf and Chickering, 1996): an algorithm calculating an approximation of the minimax value of a game state ; and on Descent Minimax: a variant of Unbounded Minimax which consists in exploring the sequences of actions until terminal states. In comparison, Unbounded Minimax and MCTS explore a sequence of actions only until a leaf state is reached.…”

Section: Descent Minimaxmentioning

confidence: 99%

Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge

Cohen-Solal¹,

Cazenave²

2023

Preprint

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In this paper, we extend the Descent framework, which enables learning and planning in the context of two-player games with perfect information, to the framework of stochastic games.We propose two ways of doing this, the first way generalizes the search algorithm, i.e. Descent, to stochastic games and the second way approximates stochastic games by deterministic games.We then evaluate them on the game EinStein würfelt nicht! against state-of-the-art algorithms: Expectiminimax and Polygames (i.e. the Alpha Zero algorithm). It is our generalization of Descent which obtains the best results. The approximation by deterministic games nevertheless obtains good results, presaging that it could give better results in particular contexts.

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Section: Descent Minimaxmentioning

confidence: 99%

Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge

Cohen-Solal¹,

Cazenave²

2023

Preprint

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“…PNS explores in first the node with the lowest proof number. Best-First Search [Korf and Chickering, 1994] calls the evaluation function for all child nodes and explores the best node first. SSS* [Stockman, 1979] explores all nodes in parallel as A* would do it with a specific heuristic.…”

Section: Best First Searchmentioning

confidence: 99%

Artificial Intelligence for Games

Bouzy

Cazenave

Corruble³

et al. 2020

A Guided Tour of Artificial Intelligence Research

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Unlike AlphaZero-like algorithms (Silver et al, 2018), the Descent framework uses a variant of Unbounded Minimax (Korf and Chickering, 1996), instead of Monte Carlo Tree Search, to construct the partial game tree used to determine the best action to play and to collect data for learning. During training, at each move, the best sequences of moves are iteratively extended until terminal states.…”

mentioning

confidence: 99%