A Planning Approach to Declarer Play in Contract Bridge

Smith, Stephen J. J.; Nau, Dana S.; Throop, Thomas A.

doi:10.1111/j.1467-8640.1996.tb00255.x

Cited by 13 publications

(11 citation statements)

References 21 publications

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“…A commercial computer bridge program [3,4] was used as the opponent in order to make the evaluation objective. Figure 3 shows the hands, and Table 1 the auction results and the scores.…”

Section: Methodsmentioning

confidence: 99%

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Cooperation and competition of agents in the auction of computer bridge

Ando¹,

Kobayashi²,

Uehara³

2003

Electron Comm Jpn Pt III

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SUMMARYThe auction in contract bridge is a game with incomplete information. This paper models the process as an interaction among agents with a hypothetical reasoning mechanism. The criterion for the action of each agent is defined as "maximizing gain by cooperating with the partner and minimizing loss by competing with the opponents." An agent with the following characteristics is created. Based on the course of bidding, the hands of both sides are estimated by hypothetical reasoning. Then flexible bids are made in an effort to obtain the contract with the greatest gain or smallest loss of points. Experiments show that in a game with incomplete information, it is useful not only to send information to the partner, but also to disrupt information exchange by the opponents. The effect of ambiguity of information on the behavior of both sides is discussed.

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“…A commercial computer bridge program [3,4] was used as the opponent in order to make the evaluation objective. Figure 3 shows the hands, and Table 1 the auction results and the scores.…”

Section: Methodsmentioning

confidence: 99%

“…GIB [3], the current computer bridge champion, utilizes this database. Bridge Baron [4], a two-time champion in the past, has a detailed flowchart for bidding which will be made available on request.…”

Section: Introductionmentioning

confidence: 99%

Cooperation and competition of agents in the auction of computer bridge

Ando¹,

Kobayashi²,

Uehara³

2003

Electron Comm Jpn Pt III

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“…Smith and Nau analyzed the game-tree search algorithms for games with imperfect information and created the Tignum system using the hierarchical task-network planning, which was able to generate significantly reduced game trees [38]- [40]. Their ideas were successfully incorporated into Bridge Baron [41], [42], the best computer bridge program in the mid 1990s.…”

Section: Previous Workmentioning

confidence: 99%

Learning Without Human Expertise: A Case Study of the Double Dummy Bridge Problem

Mossakowski

Mańdziuk

2009

IEEE Trans. Neural Netw.

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Artificial neural networks, trained only on sample deals, without presentation of any human knowledge or even rules of the game, are used to estimate the number of tricks to be taken by one pair of bridge players in the so-called double dummy bridge problem (DDBP). Four representations of a deal in the input layer were tested leading to significant differences in achieved results. In order to test networks' abilities to extract knowledge from sample deals, experiments with additional inputs representing estimators of hand's strength used by humans were also performed. The superior network trained solely on sample deals outperformed all other architectures, including those using explicit human knowledge of the game of bridge. Considering the suit contracts, this network, in a sample of 100,000 testing deals, output a perfect answer in 53.11% of the cases and only in 3.52% of them was mistaken by more than one trick. The respective figures for notrump contracts were equal to 37.80% and 16.36%. The above results were compared with the ones obtained by 24 professional human bridge players--members of The Polish Bridge Union--on test sets of sizes between 27 and 864 deals per player (depending on player's time availability). In case of suit contracts, the perfect answer was obtained in 53.06% of the testing deals for ten upper-classified players and in 48.66% of them, for the remaining 14 participants of the experiment. For the notrump contracts, the respective figures were equal to 73.68% and 60.78%. Except for checking the ability of neural networks in solving the DDBP, the other goal of this research was to analyze connection weights in trained networks in a quest for weights' patterns that are explainable by experienced human bridge players. Quite surprisingly, several such patterns were discovered (e.g., preference for groups of honors, drawing special attention to Aces, favoring cards from a trump suit, gradual importance of cards in one suit--from two to the Ace, etc.). Both the numerical figures and weight patterns are stable and repeatable in a sample of neural architectures (differing only by randomly chosen initial weights). In summary, the piece of research described in this paper provides a detailed comparison between various data representations of the DDBP solved by neural networks. On a more general note, this approach can be extended to a certain class of binary classification problems.

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“…With very few exceptions, including the Bridge Baron R and F.E.A.R. R systems, both of which use AI planning techniques [13,11], there have been very few fielded applications of these techniques into modern commercial games. Our work showcases some of these difficulties:…”

Section: Final Remarksmentioning

confidence: 99%

Game AI for Domination Games

Hogg

Lee-Urban²,

Muñoz-Ávila³

et al. 2011

Artificial Intelligence for Computer Games

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In this paper we present an overview of several techniques we have studied over the years to build game AI for domination games. Domination is a game style in which teams compete for control of map locations, and has been very popular over the years. Due to the rules of the games, good performance is mostly dependent on overall strategy rather than the skill of individual team members. Hence, this makes domination games an ideal testbed to study game AI.

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A Planning Approach to Declarer Play in Contract Bridge

Cited by 13 publications

References 21 publications

Cooperation and competition of agents in the auction of computer bridge

Cooperation and competition of agents in the auction of computer bridge

Learning Without Human Expertise: A Case Study of the Double Dummy Bridge Problem

Game AI for Domination Games

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