Author name cluster

Ian Watson

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers

1 author row

IJCAI Conference 2013 Conference Paper

Decision Generalisation from Game Logs in No Limit Texas Hold'em

Jonathan Rubin
Ian Watson

Given a set of data, recorded by observing the decisions of an expert player, we present a case-based framework that allows the successful generalisation of those decisions in the game of no limit Texas Hold’em. We address the problems of determining a suitable action abstraction and the resulting state translation that is required to map real-value bet amounts into a discrete set of abstract actions. We also detail the similarity metrics used in order to identify similar scenarios, without which no generalisation of playing decisions would be possible. We show that we were able to successfully generalise no limit betting decisions from recorded data via our agent, SartreNL, which achieved a 5th place finish out of 11 opponents at the 2012 Annual Computer Poker Competition.

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AIJ Journal 2011 Journal Article

Computer poker: A review

Jonathan Rubin
Ian Watson

The game of poker has been identified as a beneficial domain for current AI research because of the properties it possesses such as the need to deal with hidden information and stochasticity. The identification of poker as a useful research domain has inevitably resulted in increased attention from academic researchers who have pursued many separate avenues of research in the area of computer poker. The poker domain has often featured in previous review papers that focus on games in general, however a comprehensive review paper with a specific focus on computer poker has so far been lacking in the literature. In this paper, we present a review of recent algorithms and approaches in the area of computer poker, along with a survey of the autonomous poker agents that have resulted from this research. We begin with the first serious attempts to create strong computerised poker players by constructing knowledge-based and simulation-based systems. This is followed by the use of computational game theory to construct robust poker agents and the advances that have been made in this area. Approaches to constructing exploitive agents are reviewed and the challenging problems of creating accurate and dynamic opponent models are addressed. Finally, we conclude with a selection of alternative approaches that have received attention in previously published material and the interesting problems that they pose.

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IJCAI Conference 2011 Conference Paper

On Combining Decisions from Multiple Expert Imitators for Performance

Jonathan Rubin
Ian Watson

One approach for artificially intelligent agents wishing to maximise some performance metric in a given domain is to learn from a collection of training data that consists of actions or decisions made by some expert, in an attempt to imitate that expert's style. We refer to this type of agent as an expert imitator. In this paper we investigate whether performance can be improved by combining decisions from multiple expert imitators. In particular, we investigate two existing approaches for combining decisions. The first approach combines decisions by employing ensemble voting between multiple expert imitators. The second approach dynamically selects the best imitator to use at runtime given the performance of the imitators in the current environment. We investigate these approaches in the domain of computer poker. In particular, we create expert imitators for limit and no limit Texas Hold'em and determine whether their performance can be improved by combining their decisions using the two approaches listed above.

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KER Journal 2005 Journal Article

Case-based reasoning commentaries: introduction

David W. Aha
Cindy Marling
Ian Watson

We are delighted to present this special issue of The Knowledge Engineering Review, as it marks a significant accomplishment of the case-based reasoning (CBR) community. Its 19 commentaries, written by 41 authors, represent a compendium on the state-of-the-art in CBR. These evolved from a 2003 workshop that was held at Waiheke Island and Queenstown, New Zealand and chaired by Alec Holt and Ian Watson. The workshop's delegates identified the primary topics of CBR research and application, selected representative influential publications for each topic, and were encouraged to co-author commentaries on each topic with other CBR experts who were unable to attend. These collaborations produced the articles you now see. While several reviews exist on CBR (e.g. Marir & Watson, 1994; López de Mántaras & Plaza, 1997; Lenz et al., 1998), few have been published recently or have similar historical and subject breadth.

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KER Journal 2005 Journal Article

Fielded applications of case-based reasoning

WILLIAM CHEETHAM
Ian Watson

This commentary describes notable commercial applications of case-based reasoning, including systems that have been in continuous profitable use for over a decade. It is divided into sections on engineering applications, helpdesk applications and on-line case-based reasoning.

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KER Journal 2005 Journal Article

Retrieval, reuse, revision and retention in case-based reasoning

Ramon Lopez de Mantaras
David McSherry
Derek Bridge
David Leake
Barry Smyth
Susan Craw
Boi Faltings
Mary Lou Maher

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

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IJCAI Conference 1999 Conference Paper

A Distributed Case-Based Reasoning Application for Engineering Sales Support

Ian Watson
Dan Gardingen

This paper describes the implementation of a distributed case-based reasoning application that supports engineering sales staff. The application operates on the world wide web and uses the XML standard as a communications protocol between client and server side Java applets. The paper describes the distributed architecture of the application, the two case retrieval techniques used, its implementation, trial, roll-out and subsequent improvements to its architecture and retrieval techniques using introspective reasoning to improve retrieval efficiency. The benefits it has provided to the company are detailed.

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KER Journal 1994 Journal Article

Case-based reasoning: a categorized bibliography

Farhi Marir
Ian Watson

Case-Based Reasoning (CBR) is a fresh reasoning paradigm for the design of expert systems in domains that may not be appropriate for other reasoning paradigms such as model-based reasoning. As a result of this, and because of its resemblance to human reasoning, CBR has attracted increasing interest both from those experienced in developing expert systems and from novices. Although CBR is a relatively new discipline, there are an increasing number of papers and books being published on the subject. In this context, this bibliographic categorization is an accompanying paper to a review of CBR by the same authors. The objective of this paper is to help researchers quickly identify relevant references.

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KER Journal 1994 Journal Article

Case-based reasoning: A review

Ian Watson
Farhi Marir

Abstract Case-Based Reasoning (CBR) is a relatively recent problem solving technique that is attracting increasing attention. However, the number of people with first-hand theoretical or practical experience of CBR is still small. The main objective of this review is to provide a comprehensive overview of the subject to people new to CBR. The paper outlines the development of CBR in the US in the 1980s. It describes the fundamental techniques of CBR and contrasts its approach to that of model-based reasoning systems. 1 A critical review of currently available CBR software tools is followed by descriptions of CBR applications both from academic research and, in more detail, three CBR systems that are presently being used commercially. Each of the three commercial case studies highlights features that made CBR particularly suitable for the application. Moreover, the last case study describes a development methodology for implementing CBR systems. The paper concludes with a research agenda for CBR. A detailed categorized bibliography of CBR research is provided in a companion paper (Marir & Watson, 1994).

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