32 research outputs found

    The Effectiveness of Using a Modified “Beat Frequent Pick” Algorithm in the First International RoShamBo Tournament

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    In this study, a bot is developed to compete in the first International RoShamBo Tournament test suite. The basic “Beat Frequent Pick (BFP)” algorithm was taken from the supplied test suite and was improved by adding a random choice tailored fit against the opponent\u27s distribution of picks. A training program was also developed that finds the best performing bot variant by changing the bot\u27s behavior in terms of the timing of the recomputation of the pick distribution. Simulation results demonstrate the significantly improved performance of the proposed variant over the original BFP. This indicates the potential of using the core technique (of the proposed variant) as an Artificial Intelligence bot to similarly applicable computer games

    Adaptative play in texas hold'em poker

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    International audienceWe present a Texas Hold'em poker player for limit heads-up games. Our bot is designed to adapt automatically to the strategy of the opponent and is not based on Nash equilibrium computation. The main idea is to design a bot that builds beliefs on his opponent's hand. A forest of game trees is generated according to those beliefs and the solutions of the trees are combined to make the best decision. The beliefs are updated during the game according to several methods, each of which corresponding to a basic strategy. We then use an exploration-exploitation bandit algorithm, namely the UCB (Upper Confidence Bound), to select a strategy to follow. This results in a global play that takes into account the opponent's strategy, and which turns out to be rather unpredictable. Indeed, if a given strategy is exploited by an opponent, the UCB algorithm will detect it using change point detection, and will choose another one. The initial resulting program , called Brennus, participated to the AAAI'07 Computer Poker Competition in both online and equilibrium competition and ranked eight out of seventeen competitors

    SiMAMT: A Framework for Strategy-Based Multi-Agent Multi-Team Systems

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    Multi-agent multi-team systems are commonly seen in environments where hierarchical layers of goals are at play. For example, theater-wide combat scenarios where multiple levels of command and control are required for proper execution of goals from the general to the foot soldier. Similar structures can be seen in game environments, where agents work together as teams to compete with other teams. The different agents within the same team must, while maintaining their own ‘personality’, work together and coordinate with each other to achieve a common team goal. This research develops strategy-based multi-agent multi-team systems, where strategy is framed as an instrument at the team level to coordinate the multiple agents of a team in a cohesive way. A formal specification of strategy and strategy-based multi-agent multi-team systems is provided. A framework is developed called SiMAMT (strategy- based multi-agent multi-team systems). The different components of the framework, including strategy simulation, strategy inference, strategy evaluation, and strategy selection are described. A graph-matching approximation algorithm is also developed to support effective and efficient strategy inference. Examples and experimental results are given throughout to illustrate the proposed framework, including each of its composite elements, and its overall efficacy. This research make several contributions to the field of multi-agent multi-team systems: a specification for strategy and strategy-based systems, and a framework for implementing them in real-world, interactive-time scenarios; a robust simulation space for such complex and intricate interaction; an approximation algorithm that allows for strategy inference within these systems in interactive-time; experimental results that verify the various sub-elements along with a full-scale integration experiment showing the efficacy of the proposed framework

    A Paradigm Shift from Optimal Play to Mental Comfort: A Perspective from the Game Refinement Theory

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    The game refinement theory focuses on the game designer perspective, where its application in various types of games provides evidence of the occurring paradigm shift. Utilizing the logistical model of game outcome uncertainty, it provides a platform for incorporating gamified experience observed in games to be adopted in domains outside of game while retaining the context of the game. Making games as a testbed, the implications of the game refinement theory have been observed in the educational and business perspective, while further explored its utility in interpreting some states of the human mind. In addition, a holistic view of design in games and in the real-world environments was discussed, where the prospects of the game refinement theory were also highlighted

    Quality is not strategy : Nash equilibrium and international market entry

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    Version of RecordA recent Harvard Business Review article by Suarez and Lanzolla (2001) entitled the Half-truth of First Mover Advantage argued that this is a business concept which has so much intuitive appeal that its validity is almost taken for granted. In the following paper, we illustrate how typical applications of game theory to describe first mover advantage in the context of international markets are generally set up use an improper theoretical framework and compare incommensurable qualities and quantities. We then review the work of Porter (1996) and others with respect to sustainable competitive advantage and suggest that the Nash equilibrium may provide some guidance as to the kinds of circumstances in which a profitable first mover advantage may or may not be obtainable when entering international markets.Vos Fellman, P., Nugent N., Vos Post, J., & Doyon, D. (2007, October). Quality is not strategy : Nash equilibrium and international market entry. Presented at the Academy of International Business U.S. Northeast Chapter Regional Meeting, Portsmouth, New Hampshire. Retrieved from http://academicarchive.snhu.ed

    A Survey of Monte Carlo Tree Search Methods

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    Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
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