490 research outputs found

    Narrating artificial intelligence:The story of AlphaGo

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    TsetlinGo : Solving the game of Go with Tsetlin Machine

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    Master's thesis in Information- and communication technology (IKT590)The Tsetlin Machine have already shown great promise on pattern recognition and text categorization. The board game GO is a highly complex game, and the Tsetlin Machine have not yet been tested extensively on strategic games like this. This thesis introduces TsetlinGO and aims to Solve the game of Go with Tsetlin Machine. For predicting the next moves a combination of Tsetlin Machine and Tree Search was used. In the thesis a 9x9 board size was used for the game of Go, to prevent the problem from becoming to complex. This thesis goes through hyper-parameter testing for classification of the Go board game. A solution with Tree Search and Tsetlin Machine combined is used to perform self-play and matches between Tsetlin Machines with different hyper-parameters. Based on the empirical results, our conclusion is that the Tsetlin Machine is more than capable for classification of the game of Go at various stages of play. Results from the experiments could be seen to achieve around 90%, while further climbing up to around 95% upon re-training. From examining the clauses, strong patterns was found that gave insight into how the machine works. The Tsetlin Machine was able to play complete games of Go, making connections on the board through use of patterns from the clauses. It was found that the size of the clauses had great impact as clauses with large patterns had trouble getting triggered in early play. The high accuracy from classification was found to not correlate with how strong the Tsetlin Machine would perform during self-play. This may indicate that producing training data directly from self-play may be required to fine tune the assessment of board positions faced during actual play. We can conclude that this thesis provide a benchmark for further research within the field of Tsetlin Machine and the game of Go

    Scaffolding Human Champions: AI as a More Competent Other

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    Artifcial intelligence (AI) has surpassed humans in a number of specialised intellectual activities—chess and Go being two of many examples. Amongst the many potential consequences of such a development, I focus on how we can utilise cutting edge AI to promote human learning. The purpose of this article is to explore how a specialised AI can be utilised in a manner that promotes human growth by acting as a tutor to our champions. A framework for using AI as a tutor of human champions based on Vygotsky’s theory of human learning is here presented. It is based on a philosophical analysis of AI capabilities, key aspects of Vygotsky’s theory of human learning, and existing research on intelligent tutoring systems. The main method employed is the theoretical development of a generalised framework for AI powered expert learning systems, using chess and Go as examples. In addition to this, data from public interviews with top professionals in the games of chess and Go are used to examine the feasibility and realism of using AI in such a manner. Basing the analysis on Vygotsky’s socio-cultural theory of development, I explain how AI operates in the zone of proximal development of our champions and how even non-educational AI systems can perform certain scafolding functions. I then argue that AI combined with basic modules from intelligent tutoring systems could perform even more scafolding functions, but that the most interesting constellation right now is scafolding by a group consisting of AI in combination with human peers and instructors.publishedVersio

    Adaptive Neural Network Usage in Computer Go

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    For decades, computer scientists have worked to develop an artificial intelligence for the game of Go intelligent enough to beat skilled human players. In 2016, Google accomplished just that with their program, AlphaGo. AlphaGo was a huge leap forward in artificial intelligence, but required quite a lot of computational power to run. The goal of our project was to take some of the techniques that make AlphaGo so powerful, and integrate them with a less resource intensive artificial intelligence. Specifically, we expanded on the work of last year’s MQP of integrating a neural network into an existing Go AI, Pachi. We rigorously tested the resultant program’s performance. We also used SPSA training to determine an adaptive value function so as to make the best use of the neural network

    Assessing the Potential of Classical Q-learning in General Game Playing

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    After the recent groundbreaking results of AlphaGo and AlphaZero, we have seen strong interests in deep reinforcement learning and artificial general intelligence (AGI) in game playing. However, deep learning is resource-intensive and the theory is not yet well developed. For small games, simple classical table-based Q-learning might still be the algorithm of choice. General Game Playing (GGP) provides a good testbed for reinforcement learning to research AGI. Q-learning is one of the canonical reinforcement learning methods, and has been used by (Banerjee &\& Stone, IJCAI 2007) in GGP. In this paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe, Connect Four, Hex)\footnote{source code: https://github.com/wh1992v/ggp-rl}, to allow comparison to Banerjee et al.. We find that Q-learning converges to a high win rate in GGP. For the ϵ\epsilon-greedy strategy, we propose a first enhancement, the dynamic ϵ\epsilon algorithm. In addition, inspired by (Gelly &\& Silver, ICML 2007) we combine online search (Monte Carlo Search) to enhance offline learning, and propose QM-learning for GGP. Both enhancements improve the performance of classical Q-learning. In this work, GGP allows us to show, if augmented by appropriate enhancements, that classical table-based Q-learning can perform well in small games.Comment: arXiv admin note: substantial text overlap with arXiv:1802.0594
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