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Ian Frank
Researcher at Future University Hakodate
Publications - 42
Citations - 778
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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Journal ArticleDOI
Soccer server: A tool for research on multiagent systems
TL;DR: The potential of Soccer Server is demonstrated by reporting an experiment that uses the system to compare the performance of a neural network architecture and a decision tree algorithm at learning the selection of soccer play plans.
Journal ArticleDOI
Search in games with incomplete information: a case study using Bridge card play
Ian Frank,David Basin +1 more
TL;DR: It is shown that equilibrium point strategies for optimal play exist for this model, and an algorithm capable of computing such strategies is defined, and this model allows for clearly state the limitations of such architectures in producing expert analysis.
Proceedings ArticleDOI
MIKE: an automatic commentary system for soccer
TL;DR: This paper describes MIKE, an automatic commentary system for the game of soccer that interprets this domain with six soccer analysis modules that run concurrently within a role-sharing framework and discusses how to control the interaction between them.
Proceedings ArticleDOI
Making recipes in the kitchen of the future
TL;DR: This contribution is to demonstrate how a “Kitchen of the Future” can use technology to re-introduce such social interactions, and also enable entirely novel forms of communication mediated by computer.
Proceedings Article
Finding optimal strategies for imperfect information games
TL;DR: These algorithms theoretically and experimentally are compared using both simple game trees and a large database of problems from the game of Bridge, showing that the new algorithms both out-perform Monte-carlo sampling, with the superiority of payoff-reduction minimaxing being especially marked.