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Ian Frank

Bio: Ian Frank is an academic researcher from Future University Hakodate. The author has contributed to research in topics: Game tree & Complete information. The author has an hindex of 13, co-authored 40 publications receiving 756 citations. Previous affiliations of Ian Frank include Vrije Universiteit Brussel & Future University in Egypt.

Papers
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Book
09 Jan 2002
TL;DR: A Least-Certainty Heuristic for Selective Search for Game Trees, and Linguistic Geometry for Solving War Games.
Abstract: Search and Strategies.- A Least-Certainty Heuristic for Selective Search.- Lambda-Search in Game Trees - with Application to Go.- Abstract Proof Search.- Solving Kriegspiel-Like Problems: Examining Efficient Search Methods.- Strategies for the Automatic Construction of Opening Books.- Awari Retrograde Analysis.- Construction of Chinese Chess Endgame Databases by Retrograde Analysis.- Learning and Pattern Acquisition.- Learning from Perfection.- Chess Neighborhoods, Function Combination, and Reinforcement Learning.- Learning a Go Heuristic with Tilde.- Learning Time Allocation Using Neural Networks.- Theory and Complexity Issues.- The Complexity of Graph Ramsey Games.- Virus Versus Mankind.- Creating Difficult Instances of the Post Correspondence Problem.- Integer Programming Based Algorithms for Peg Solitaire Problems.- Ladders Are PSPACE-Complete.- Simple Amazons Endgames and Their Connection to Hamilton Circuits in Cubic Subgrid Graphs.- Further Experiments with Games.- New Self-Play Results in Computer Chess.- SUPER-SOMA - Solving Tactical Exchanges in Shogi without Tree Searching.- A Shogi Processor with a Field Programmable Gate Array.- Plausible Move Generation Using Move Merit Analysis with Cut-Off Thresholds in Shogi.- Abstraction Methods for Game Theoretic Poker.- Reasoning by Agents in Computer Bridge Bidding.- Invited Talks and Reviews.- Linguistic Geometry for Solving War Games.- Physics and Ecology of Rock-Paper-Scissors Game.- Review: Computer Language Games.- Review: Computer Go 1984-2000.- Review: Intelligent Agents for Computer Games.- Review: RoboCup through 2000.- Review: Computer Shogi through 2000.

2 citations

Journal ArticleDOI
TL;DR: This paper reviews the developments in evolvable hardware systems presented at the First International Conference on Evolvable Systems (ICES 96) and splits them into three broad groups according to whether they involve evolving a fit solution to a problem as a member of a population of competing candidates, evolving solutions that can individually learn from and adapt to their environments, or the embryonic growth of solutions.
Abstract: This paper reviews the developments in evolvable hardware systems presented at the First International Conference on Evolvable Systems (ICES 96). The main body of the review gives an overview of the 34 papers presented orally, splitting them into three broad groups according to whether they involve (1) evolving a fit solution to a problem as a member of a population of competing candidates, (2) evolving solutions that can individually learn from and adapt to their environments, or (3) the embryonic growth of solutions. We also review the discussion sessions of the conference and give pointers to related upcoming events.

1 citations

Proceedings Article
01 Jan 1992

1 citations


Cited by
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Journal ArticleDOI
TL;DR: This survey of MAS is intended to serve as an introduction to the field and as an organizational framework, and highlights how multiagent systems can be and have been used to build complex systems.
Abstract: Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goals Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of general multiagent scenarios are presented. For each scenario, the issues that arise are described along with a sampling of the techniques that exist to deal with them. The presented techniques are not exhaustive, but they highlight how multiagent systems can be and have been used to build complex systems. When options exist, the techniques presented are biased towards machine learning approaches. Additional opportunities for applying machine learning to MAS are highlighted and robotic soccer is presented as an appropriate test bed for MAS. This survey does not focus exclusively on robotic systems. However, we believe that much of the prior research in non-robotic MAS is relevant to robotic MAS, and we explicitly discuss several robotic MAS, including all of those presented in this issue.

1,073 citations

Proceedings ArticleDOI
17 Apr 2007
TL;DR: This paper provides a comprehensive review of explanations in recommender systems, highlighting seven possible advantages of an explanation facility, and describing how existing measures can be used to evaluate the quality of explanations.
Abstract: This paper provides a comprehensive review of explanations in recommender systems. We highlight seven possible advantages of an explanation facility, and describe how existing measures can be used to evaluate the quality of explanations. Since explanations are not independent of the recommendation process, we consider how the ways recommendations are presented may affect explanations. Next, we look at different ways of interacting with explanations. The paper is illustrated with examples of explanations throughout, where possible from existing applications.

528 citations

Journal ArticleDOI
TL;DR: The application of episodic SMDP Sarsa(λ) with linear tile-coding function approximation and variable λ to learning higher-level decisions in a keepaway subtask of RoboCup soccer results in agents that significantly outperform a range of benchmark policies.
Abstract: RoboCup simulated soccer presents many challenges to reinforcement learning methods, including a large state space, hidden and uncertain state, multiple independent agents learning simultaneously, and long and variable delays in the effects of actions. We describe our application of episodic SMDP Sarsa(λ) with linear tile-coding function approximation and variable λ to learning higher-level decisions in a keepaway subtask of RoboCup soccer. In keepaway, one team, “the keepers,” tries to keep control of the ball for as long as possible despite the efforts of “the takers.” The keepers learn individually when to hold the ball and when to pass to a teammate. Our agents learned policies that significantly outperform a range of benchmark policies. We demonstrate the generality of our approach by applying it to a number of task variations including different field sizes and different numbers of players on each team.

430 citations

Journal ArticleDOI
09 Jan 2015-Science
TL;DR: It is announced that heads-up limit Texas hold’em is now essentially weakly solved, and this computation formally proves the common wisdom that the dealer in the game holds a substantial advantage.
Abstract: Poker is a family of games that exhibit imperfect information, where players do not have full knowledge of past events. Whereas many perfect-information games have been solved (e.g., Connect Four and checkers), no nontrivial imperfect-information game played competitively by humans has previously been solved. Here, we announce that heads-up limit Texas hold’em is now essentially weakly solved. Furthermore, this computation formally proves the common wisdom that the dealer in the game holds a substantial advantage. This result was enabled by a new algorithm, CFR + , which is capable of solving extensive-form games orders of magnitude larger than previously possible.

413 citations