Expected-outcome: a general model of static evaluation

Abramson, Bruce

doi:10.1109/34.44404

Cited by 98 publications

(71 citation statements)

References 9 publications

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“…Beyond the raw numbers, it is interesting to take a look at the games, and the playing styles of the different players 1 . Most of the losses of Crazy Stone against GNU Go are due to tactics that are too deep, such as ladders, long semeais, and monkey jumps, that GNU Go has no difficulty to see.…”

Section: Game Resultsmentioning

confidence: 99%

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Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

Coulom

2007

Lecture Notes in Computer Science

902

770

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Abstract. Monte-Carlo evaluation consists in estimating a position by averaging the outcome of several random continuations, and can serve as an evaluation function at the leaves of a min-max tree. This paper presents a new framework to combine tree search with Monte-Carlo evaluation, that does not separate between a min-max phase and a MonteCarlo phase. Instead of backing-up the min-max value close to the root, and the average value at some depth, a more general backup operator is defined that progressively changes from averaging to min-max as the number of simulations grows. This approach provides a fine-grained control of the tree growth, at the level of individual simulations, and allows efficient selectivity methods. This algorithm was implemented in a 9 × 9 Go-playing program, Crazy Stone, that won the 10th KGS computer-Go tournament.

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Section: Game Resultsmentioning

confidence: 99%

“…Monte-Carlo evaluation consists in averaging the outcome of several continuations. It is an usual technique in games with randomness or partial observability [5,23,26,14,17], but can also be applied to deterministic games, by choosing actions at random until a terminal state is reached [1,9,10].…”

Section: Introductionmentioning

confidence: 99%

Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

Coulom

2007

Lecture Notes in Computer Science

902

770

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“…In section (3) we introduce a Monte Carlo Stochastic Diffusion Search (MCSDS), a swarm intelligence algorithm for playing Hex based on a simple merger of Stochastic Diffusion Search (SDS) [4] and Monte Carlo methods [25]. Subsequently extending MCSDS in Section (4), we introduce a more sophisticated algorithm, Stochastic Diffusion Search applied to Trees (SDST); a novel swarm intelligence heuristic able to solve the complex and general problem of forward planning in a way analogous to Monte-Carlo Tree Search (MCTS) [1].…”

Section: Swarm Intelligencementioning

confidence: 99%

“…MCTS has originally been developed in the context of computer game playing and finds its roots in B. Abramson's 1990 paper Expected-outcome: a general model of static evaluation [1]. This paper introduces the central Monte Carlo theme to 6 A 'game tree' is a directed graph whose nodes are positions in a game and whose edges are moves.…”

Section: Monte-carlo Tree Search (Mcts)mentioning

confidence: 99%

HeX and the Single Anthill: Playing Games with Aunt Hillary

Bishop

Nasuto

Tanay

et al. 2016

Fundamental Issues of Artificial Intelligence

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In a reflective and richly entertaining piece from 1979, Doug Hofstadter playfully imagined a conversation between 'Achilles' and an anthill (the eponymous 'Aunt Hillary'), in which he famously explored many ideas and themes related to cognition and consciousness. For Hofstadter, the anthill is able to carry on a conversation because the ants that compose it play roughly the same role that neurons play in human languaging; unfortunately, Hofstadter's work is notably short on detail suggesting how this magic might be achieved 1 . Conversely in this paper -finally reifying Hofstadter's imagination -we demonstrate how populations of simple ant-like creatures can be organised to solve complex problems; problems that involve the use of forward planning and strategy. Specifically we will demonstrate that populations of such creatures can be configured to play a strategically strongthough tactically weak -game of HeX (a complex strategic game). We subsequently demonstrate how tactical play can be improved by introducing a form of forward planning instantiated via multiple populations of agents; a technique that can be compared to the dynamics of interacting populations of social insects via the concept of meta-population. In this way although, pace Hofstadter, we do not establish that a meta-population of ants could actually hold a conversation with Achilles, we do successfully introduce Aunt Hillary to the complex, seductive charms of HeX.J. Mark Bishop Goldsmiths, University of London, UK, e-mail: m.bishop@gold.ac.uk 1 As Drew McDermott writes in the Cambridge Handbook of Consciousness [24], it is as if Hofstadter "wants to invent a new, playful style of argumentation, in which concepts are broken up and tossed together into so many configurations that the original question one might have asked get shunted aside".

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“…This is achieved by running a huge number of random games from a given position and by averaging their results, thus computing the approximation we need. It has been proposed in 1990 by Bruce Abramson [10].…”

Section: B Solversmentioning

confidence: 99%

A comparative study of solvers in Amazons endgames

Kloetzer

Iida

Bouzy

2008

2008 IEEE Symposium on Computational Intelligence and Games

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Abstract-The game of Amazons is a fairly young member of the class of territory-games. The best Amazons programs play now at a high level, but can still be defeated by humans expert of the game. Our focus here is on the solving of endgames, with the goal of improving the general playing level of Amazons programs. Our comparative study of four solvers, DFPN, WPNS, Alpha/Beta and Monte-Carlo Tree-Search, shows that usual game-playing methods used for Amazons, namely AlphaBeta and Monte-Carlo Tree-Search, are best suited for this task. This result also emphasizes the need of an evaluation function for the game of Amazons.

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Expected-outcome: a general model of static evaluation

Cited by 98 publications

References 9 publications

Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

HeX and the Single Anthill: Playing Games with Aunt Hillary

A comparative study of solvers in Amazons endgames

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