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.

Review: RoboCup through 2000

Abstract: Can a team of robots beat the human world champions at soccer? That is the 50-year grand challenge at the heart of the Robotic Soccer World Cup (RoboCup) initiative. Every year, researchers from around the world gather at the RoboCup tournaments to test their teams of software and hardware soccer players against each other. We report here on the first three of these tournaments, which were held in 1997 (Nagoya), 1998 (Paris) and 1999 (Stockholm). We summarise the game results, the practical and scientific lessons learned, and the progress towards that grand challenge goal.

Review: RoboCup through 2000

Abstract: Can a team of robots beat the human world champions at soccer? That is the 50-year grand challenge at the heart of the Robotic Soccer World Cup (RoboCup) initiative. Every year, researchers from around the world gather at the RoboCup tournaments to test their teams of software and hardware soccer players against each other. We report here on the first three of these tournaments, which were held in 1997 (Nagoya), 1998 (Paris) and 1999 (Stockholm). We summarise the game results, the practical and scientific lessons learned, and the progress towards that grand challenge goal.

Linking University Curriculum and Community - Learn Programming with Scratch, Get Projection

Abstract: We use the Scratch programming environment to attract and to teach young students the basics of programming, and as a by-product to create animations that can engage the public as media installations. We especially plan to leverage our existing collaboration with an international music festival. Based on this year's experience, we will consider what other aspects of a curriculum (such as music, complex systems, design) could be suitable for taking into the community in this way.

Learning From Events

Abstract: What can be learned from the successful production of large-scale real-world arts events that is useful in the classroom? Through practical examples, this paper attempts to make some connections. We start with a short story of how the same software came to be used to deliver university-level classes on AI and also co-ordinate the international

Multiagent Systems: A Survey from a Machine Learning Perspective

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.

A Survey of Explanations in Recommender Systems

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.

Reinforcement learning for RoboCup soccer keepaway

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.

Heads-up limit hold’em poker is solved

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.