The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments

International audienceTHE AUTHORS ARE EXTREMELY GRATEFUL TO GRID5000 for helping in designing and experimenting around Monte-Carlo Tree Search. In order to promote computer Go and stimulate further development and research in the field, the event activities, "Computational Intelligence Forum" and "World 99 Computer Go Championship," were held in Taiwan. This study focuses on the invited games played in the tournament, "Taiwanese Go players versus the computer program MoGo," held at National University of Tainan (NUTN). Several Taiwanese Go players, including one 9-Dan professional Go player and eight amateur Go players, were invited by NUTN to play against MoGo from August 26 to October 4, 2008. The MoGo program combines All Moves As First (AMAF)/Rapid Action Value Estimation (RAVE) values, online "UCT-like" values, offline values extracted from databases, and expert rules. Additionally, four properties of MoGo are analyzed including: (1) the weakness in corners, (2) the scaling over time, (3) the behavior in handicap games, and (4) the main strength of MoGo in contact fights. The results reveal that MoGo can reach the level of 3 Dan with, (1) good skills for fights, (2) weaknesses in corners, in particular for "semeai" situations, and (3) weaknesses in favorable situations such as handicap games. It is hoped that the advances in artificial intelligence and computational power will enable considerable progress in the field of computer Go, with the aim of achieving the same levels as computer chess or Chinese chess in the future

Application of temporal difference learning and supervised learning in the game of Go.

by Horace Wai-Kit, Chan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 109-112).Acknowledgement --- p.iAbstract --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Objective --- p.3Chapter 1.3 --- Organization of This Thesis --- p.3Chapter 2 --- Background --- p.5Chapter 2.1 --- Definitions --- p.5Chapter 2.1.1 --- Theoretical Definition of Solving a Game --- p.5Chapter 2.1.2 --- Definition of Computer Go --- p.7Chapter 2.2 --- State of the Art of Computer Go --- p.7Chapter 2.3 --- A Framework for Computer Go --- p.11Chapter 2.3.1 --- Evaluation Function --- p.11Chapter 2.3.2 --- Plausible Move Generator --- p.14Chapter 2.4 --- Problems Tackled in this Research --- p.14Chapter 3 --- Application of TD in Game Playing --- p.15Chapter 3.1 --- Introduction --- p.15Chapter 3.2 --- Reinforcement Learning and TD Learning --- p.15Chapter 3.2.1 --- Models of Learning --- p.16Chapter 3.2.2 --- Temporal Difference Learning --- p.16Chapter 3.3 --- TD Learning and Game-playing --- p.20Chapter 3.3.1 --- Game-Playing as a Delay-reward Prediction Problem --- p.20Chapter 3.3.2 --- Previous Work of TD Learning in Backgammon --- p.20Chapter 3.3.3 --- Previous Works of TD Learning in Go --- p.22Chapter 3.4 --- Design of this Research --- p.23Chapter 3.4.1 --- Limitations in the Previous Researches --- p.24Chapter 3.4.2 --- Motivation --- p.25Chapter 3.4.3 --- Objective and Methodology --- p.26Chapter 4 --- Deriving a New Updating Rule to Apply TD Learning in Multi-layer Perceptron --- p.28Chapter 4.1 --- Multi-layer Perceptron (MLP) --- p.28Chapter 4.2 --- Derivation of TD(A) Learning Rule for MLP --- p.31Chapter 4.2.1 --- Notations --- p.31Chapter 4.2.2 --- A New Generalized Delta Rule --- p.31Chapter 4.2.3 --- Updating rule for TD(A) Learning --- p.34Chapter 4.3 --- Algorithm of Training MLP using TD(A) --- p.35Chapter 4.3.1 --- Definitions of Variables in the Algorithm --- p.35Chapter 4.3.2 --- Training Algorithm --- p.36Chapter 4.3.3 --- Description of the Algorithm --- p.39Chapter 5 --- Experiments --- p.41Chapter 5.1 --- Introduction --- p.41Chapter 5.2 --- Experiment 1 : Training Evaluation Function for 7 x 7 Go Games by TD(λ) with Self-playing --- p.42Chapter 5.2.1 --- Introduction --- p.42Chapter 5.2.2 --- 7 x 7 Go --- p.42Chapter 5.2.3 --- Experimental Designs --- p.43Chapter 5.2.4 --- Performance Testing for Trained Networks --- p.44Chapter 5.2.5 --- Results --- p.44Chapter 5.2.6 --- Discussions --- p.45Chapter 5.2.7 --- Limitations --- p.47Chapter 5.3 --- Experiment 2 : Training Evaluation Function for 9 x 9 Go Games by TD(λ) Learning from Human Games --- p.47Chapter 5.3.1 --- Introduction --- p.47Chapter 5.3.2 --- 9x 9 Go game --- p.48Chapter 5.3.3 --- Training Data Preparation --- p.49Chapter 5.3.4 --- Experimental Designs --- p.50Chapter 5.3.5 --- Results --- p.52Chapter 5.3.6 --- Discussion --- p.54Chapter 5.3.7 --- Limitations --- p.56Chapter 5.4 --- Experiment 3 : Life Status Determination in the Go Endgame --- p.57Chapter 5.4.1 --- Introduction --- p.57Chapter 5.4.2 --- Training Data Preparation --- p.58Chapter 5.4.3 --- Experimental Designs --- p.60Chapter 5.4.4 --- Results --- p.64Chapter 5.4.5 --- Discussion --- p.65Chapter 5.4.6 --- Limitations --- p.66Chapter 5.5 --- A Postulated Model --- p.66Chapter 6 --- Conclusions --- p.69Chapter 6.1 --- Future Direction of Research --- p.71Chapter A --- An Introduction to Go --- p.72Chapter A.l --- A Brief Introduction --- p.72Chapter A.1.1 --- What is Go? --- p.72Chapter A.1.2 --- History of Go --- p.72Chapter A.1.3 --- Equipment used in a Go game --- p.73Chapter A.2 --- Basic Rules in Go --- p.74Chapter A.2.1 --- A Go game --- p.74Chapter A.2.2 --- Liberty and Capture --- p.75Chapter A.2.3 --- Ko --- p.77Chapter A.2.4 --- "Eyes, Live and Death" --- p.81Chapter A.2.5 --- Seki --- p.83Chapter A.2.6 --- Endgame and Scoring --- p.83Chapter A.2.7 --- Rank and Handicap Games --- p.85Chapter A.3 --- Strategies and Tactics in Go --- p.87Chapter A.3.1 --- Strategy vs Tactics --- p.87Chapter A.3.2 --- Open-game --- p.88Chapter A.3.3 --- Middle-game --- p.91Chapter A.3.4 --- End-game --- p.92Chapter B --- Mathematical Model of Connectivity --- p.94Chapter B.1 --- Introduction --- p.94Chapter B.2 --- Basic Definitions --- p.94Chapter B.3 --- Adjacency and Connectivity --- p.96Chapter B.4 --- String and Link --- p.98Chapter B.4.1 --- String --- p.98Chapter B.4.2 --- Link --- p.98Chapter B.5 --- Liberty and Atari --- p.99Chapter B.5.1 --- Liberty --- p.99Chapter B.5.2 --- Atari --- p.101Chapter B.6 --- Ko --- p.101Chapter B.7 --- Prohibited Move --- p.104Chapter B.8 --- Path and Distance --- p.105Bibliography --- p.10

Fact, Fiction and Virtual Worlds

This paper considers the medium of videogames from a goodmanian standpoint. After some preliminary clarifications and definitions, I examine the ontological status of videogames. Against several existing accounts, I hold that what grounds their identity qua work types is code. The rest of the paper is dedicated to the epistemology of videogaming. Drawing on Nelson Goodman and Catherine Elgin's works, I suggest that the best model to defend videogame cognitivism appeals to the notion of understanding

Spatial-temporal reasoning applications of computational intelligence in the game of Go and computer networks

Spatial-temporal reasoning is the ability to reason with spatial images or information about space over time. In this dissertation, computational intelligence techniques are applied to computer Go and computer network applications. Among four experiments, the first three are related to the game of Go, and the last one concerns the routing problem in computer networks. The first experiment represents the first training of a modified cellular simultaneous recurrent network (CSRN) trained with cellular particle swarm optimization (PSO). Another contribution is the development of a comprehensive theoretical study of a 2x2 Go research platform with a certified 5 dan Go expert. The proposed architecture successfully trains a 2x2 game tree. The contribution of the second experiment is the development of a computational intelligence algorithm calledcollective cooperative learning (CCL). CCL learns the group size of Go stones on a Go board with zero knowledge by communicating only with the immediate neighbors. An analysis determines the lower bound of a design parameter that guarantees a solution. The contribution of the third experiment is the proposal of a unified system architecture for a Go robot. A prototype Go robot is implemented for the first time in the literature. The last experiment tackles a disruption-tolerant routing problem for a network suffering from link disruption. This experiment represents the first time that the disruption-tolerant routing problem has been formulated with a Markov Decision Process. In addition, the packet delivery rate has been improved under a range of link disruption levels via a reinforcement learning approach --Abstract, page iv

Player–Game Interaction and Cognitive Gameplay: A Taxonomic Framework for the Core Mechanic of Videogames

Cognitive gameplay—the cognitive dimension of a player’s experience—emerges from the interaction between a player and a game. While its design requires careful consideration, cognitive gameplay can be designed only indirectly via the design of game components. In this paper, we focus on one such component—the core mechanic—which binds a player and game together through the performance of essential interactions. Little extant research has been aimed at developing frameworks to support the design of interactions within the core mechanic with cognitive gameplay in mind. We present a taxonomic framework named INFORM (Interaction desigN For the cORe Mechanic) to address this gap. INFORM employs twelve micro-level elements that collectively give structure to any individual interaction within the core mechanic. We characterize these elements in the context of videogames, and discuss their potential influences on cognitive gameplay. We situate these elements within a broader framework that synthesizes concepts relevant to game design. INFORM is a descriptive framework, and provides a common vocabulary and a set of concepts that designers can use to think systematically about issues related to micro-level interaction design and cognitive gameplay

Player–Game Interaction and Cognitive Gameplay: A Taxonomic Framework for the Core Mechanic of Videogames

Cognitive gameplay—the cognitive dimension of a player’s experience—emerges from the interaction between a player and a game. While its design requires careful consideration, cognitive gameplay can be designed only indirectly via the design of game components. In this paper, we focus on one such component—the core mechanic—which binds a player and game together through the performance of essential interactions. Little extant research has been aimed at developing frameworks to support the design of interactions within the core mechanic with cognitive gameplay in mind. We present a taxonomic framework named INFORM (Interaction desigN For the cORe Mechanic) to address this gap. INFORM employs twelve micro-level elements that collectively give structure to any individual interaction within the core mechanic. We characterize these elements in the context of videogames, and discuss their potential influences on cognitive gameplay. We situate these elements within a broader framework that synthesizes concepts relevant to game design. INFORM is a descriptive framework, and provides a common vocabulary and a set of concepts that designers can use to think systematically about issues related to micro-level interaction design and cognitive gameplay

Scaffolding Human Champions: AI as a More Competent Other

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