8 research outputs found

    A Highly-Parameterized Ensemble to Play Gin Rummy

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    This paper describes the design and training of a computer Gin Rummy player. The system includes three main components to make decisions about drawing cards, discarding, and ending the game, with numerous parameters controlling behavior. In particular, an ensemble approach is explored in the discard decision. Finally, three sets of parameter tuning and performance experiments are analyzed

    Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

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    The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains

    Applying machine learning techniques to an imperfect information game

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    The game of poker presents a challenging game to Artificial Intelligence researchers because it is a complex asymmetric information game. In such games, a player can improve his performance by inferring the private information held by the other players from their prior actions. A novel connectionist structure was designed to play a version of poker (multi-player limit Hold‟em). This allows simple reinforcement learning techniques to be used which previously not been considered for the game of multi-player hold‟em. A related hidden Markov model was designed to be fitted to records of poker play without using any private information. Belief vectors generated by this model provide a more convenient and flexible representation of an opponent‟s action history than alternative approaches. The structure was tested in two settings. Firstly self-play simulation was used to generate an approximation to a Nash equilibrium strategy. A related, but slower, rollout strategy that uses Monte-Carlo samples was used to evaluate the performance. Secondly the structure was used to model and hence exploit a population of opponents within a relatively small number of games. When and how to adapt quickly to new opponents are open questions in poker AI research. A opponent model with a small number of discrete types is used to identify the largest differences in strategy between members of the population. A commercial software package (Poker Academy) was used to provide a population of sophisticated opponents to test against. A series of experiments was conducted to compare adaptive and static systems. All systems showed positive results but surprisingly the adaptive systems did not show a significant improvement over similar static systems. The possible reasons for this result are discussed. This work formed the basis of a series of entries to the computer poker competition hosted at the annual conferences of the Association for the Advancement of Artificial Intelligence (AAAI). Its best rankings were 3rd in the 2006 6-player limit hold‟em competition and 2nd in the 2008 3-player limit hold‟em competition

    A learning framework for zero-knowledge game playing agents

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    The subjects of perfect information games, machine learning and computational intelligence combine in an experiment that investigates a method to build the skill of a game-playing agent from zero game knowledge. The skill of a playing agent is determined by two aspects, the first is the quantity and quality of the knowledge it uses and the second aspect is its search capacity. This thesis introduces a novel representation language that combines symbols and numeric elements to capture game knowledge. Insofar search is concerned; an extension to an existing knowledge-based search method is developed. Empirical tests show an improvement over alpha-beta, especially in learning conditions where the knowledge may be weak. Current machine learning techniques as applied to game agents is reviewed. From these techniques a learning framework is established. The data-mining algorithm, ID3, and the computational intelligence technique, Particle Swarm Optimisation (PSO), form the key learning components of this framework. The classification trees produced by ID3 are subjected to new post-pruning processes specifically defined for the mentioned representation language. Different combinations of these pruning processes are tested and a dominant combination is chosen for use in the learning framework. As an extension to PSO, tournaments are introduced as a relative fitness function. A variety of alternative tournament methods are described and some experiments are conducted to evaluate these. The final design decisions are incorporated into the learning frame-work configuration, and learning experiments are conducted on Checkers and some variations of Checkers. These experiments show that learning has occurred, but also highlights the need for further development and experimentation. Some ideas in this regard conclude the thesis.Dissertation (MSc)--University of Pretoria, 2007.Computer ScienceMScUnrestricte

    The Viet Nam Generation Big Book

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    An anthology of essays, narrative, poetry and graphics published in lieu of a 1993 issue of Viet Nam Generation, intended to be used as a textbook for teaching about the 1960s. Edited by Dan Duffy and Kali Tal. Contributing editors: Renny Christopher. David DeRose, Alan Farrell. Cynthia Fuchs, William M. King. Bill Shields, Tony Williams, and David Willson

    The significance of temporal-difference learning in self-play training: TD-rummy versus EVO-rummy

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    Reinforcement learning has been used for training game playing agents. The value function for a complex game must be approximated with a continuous function because the number of states becomes too large to enumerate. Temporal-difference learning with self-play is one method successfully used to derive the value approximation function. Coevolution of the value function is also claimed to yield good results. This paper reports on a direct comparison between an agent trained to play gin rummy using temporal difference learning, and the same agent trained with co-evolution. Coevolution produced superior results. 1

    Agroecological Transitions: From Theory to Practice in Local Participatory Design

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    This Open Access book presents feedback from the ‘Territorial Agroecological Transition in Action’- TATA-BOX research project, which was devoted to these specific issues. The multidisciplinary and multi-organisation research team steered a four-year action-research process in two territories of France. It also presents: i) the key dimensions to be considered when dealing with agroecological transition: diversity of agriculture models, management of uncertainties, polycentric governance, autonomies, and role of actors’ networks; ii) an operational and original participatory process and associated boundary tools to support local stakeholders in shifting from a shared diagnosis to a shared action plan for transition, and in so doing developing mutual understanding and involvement; iii) an analysis of the main effects of the methodology on research organisation and on stakeholders’ development and application; iv) critical analysis and foresights on the main outcomes of TATA-BOX, provided by external researchers
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