Cooperation and competition of agents in the auction of computer bridge

Ando, Takahisa; Kobayashi, Noriyuki; Uehara, T.

doi:10.1002/ecjc.10144

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…In [53], each player is modeled as an independent, active agent that takes part in the communication process. Also, other researchers represented the bidding phase as cooperation of two agents [54], [55] or cooperation of agents in competing pairs [56]. In [47], an agentbased algorithm was proposed, which was able to achieve, after appropriate learning, a bidding ability close to that of a human expert.…”

Section: A the Bidding Phasementioning

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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Section: A the Bidding Phasementioning

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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“…In each, player is modeled as an autonomous, active agent that takes part in the message process. The agent-based algorithm to use of achieve in appropriate learning, a bidding ability close to that of a human expert [20], [21], [22].…”

Section: The Biddingmentioning

confidence: 99%

Artificial Neural Network Architectures for Solving the Contract Bridge

Dharmalingam

2020

International Journal of Advanced Information and Communication Technology

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Contract Bridge is an intelligent game, which enhances the creativity with multiple skills and quest to acquire the intricacies of the game, because no player knows exactly what moves other players are capable of during their turn. The Bridge being a game of imperfect information is to be equally well defined, since the outcome at any intermediate stage is purely based on the decision made on the immediate preceding stage. One among the architectures of Artificial Neural Networks (ANN) is applied by training on sample deals and used to estimate the number of tricks to be taken by one pair of bridge players is the key idea behind Double Dummy Bridge Problem (DDBP) implemented with the neural network paradigm. This study mainly focuses on Cascade-Correlation Neural Network (CCNN) and Elman Neural Network (ENN) which is used to solve the Bridge problem by using Resilient Back-Propagation (R-prop) Algorithm and Work Point Count System.

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“…Some programs, like BRIBIP (Stanier, 1975), MICROBRIDGE (Macleod, 1989), and the one developed by Ando et al (2003), tried to infer the cards of the other players on the basis of their calls. The programs applied the rules backwards to generate constraints over the hidden hands of the other players and used them both for the bidding and play stages.…”

Section: Bridge Biddingmentioning

confidence: 99%

Learning to bid in bridge

Amit

Markovitch

2006

Mach Learn

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Bridge bidding is considered to be one of the most difficult problems for gameplaying programs. It involves four agents rather than two, including a cooperative agent. In addition, the partial observability of the game makes it impossible to predict the outcome of each action. In this paper we present a new decision-making algorithm that is capable of overcoming these problems. The algorithm allows models to be used for both opponent agents and partners, while utilizing a novel model-based Monte Carlo sampling method to overcome the problem of hidden information. The paper also presents a learning framework that uses the above decision-making algorithm for co-training of partners. The agents refine their selection strategies during training and continuously exchange their refined strategies. The refinement is based on inductive learning applied to examples accumulated for classes of states with conflicting actions. The algorithm was empirically evaluated on a set of bridge deals. The pair of agents that co-trained significantly improved their bidding performance to a level surpassing that of the current state-of-the-art bidding algorithm.

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Cooperation and competition of agents in the auction of computer bridge

Cited by 5 publications

References 3 publications

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

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

Artificial Neural Network Architectures for Solving the Contract Bridge

Learning to bid in bridge

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