AI 2003: Advances in Artificial Intelligence

1. Front Matter

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2. Keynote Papers

   1. Discovery of Emerging Patterns and Their Use in Classification

      • Kotagiri Ramamohanarao, James Bailey

      Pages 1-11

   2. Robot Soccer: Science or Just Fun and Games?

      • Claude Sammut

      Pages 12-23

   3. On How to Learn from a Stochastic Teacher or a Stochastic Compulsive Liar of Unknown Identity

      • B. John Oommen, Govindachari Raghunath, Benjamin Kuipers

      Pages 24-40

   4. Multimedia Analysis and Synthesis

      • Mohan S. Kankanhalli

      Pages 41-52

3. Ontology

   1. Modelling Message Handling System

      • Insu Song, Pushkar Piggott

      Pages 53-64

   2. A New Approach for Concept-Based Web Search

      • Seung Yeol Yoo, Achim Hoffmann

      Pages 65-76

   3. Representing the Spatial Relations in the Semantic Web Ontologies

      • Hyunjang Kong, Kwanho Jung, Junho Choi, Wonpil Kim, Pankoo Kim, Jongan Park

      Pages 77-87

   4. Inductive Construction of Ontologies from Formal Concept Analysis

      • Michael Bain

      Pages 88-99

4. Problem Solving

   1. Dynamic Variable Filtering for Hard Random 3-SAT Problems

      • A. Anbulagan, John Thornton, Abdul Sattar

      Pages 100-111

   2. A Proposal of an Efficient Crossover Using Fitness Prediction and Its Application

      • Atsuko Mutoh, Tsuyoshi Nakamura, Shohei Kato, Hidenori Itoh

      Pages 112-124

   3. A New Hybrid Genetic Algorithm for the Robust Graph Coloring Problem

      • Ying Kong, Fan Wang, Andrew Lim, Songshan Guo

      Pages 125-136

   4. Estimating Problem Metrics for SAT Clause Weighting Local Search

      • Wayne Pullan, Liang Zhao, John Thornton

      Pages 137-149

5. Knowledge Discovery and Data Mining I

   1. Information Extraction via Path Merging

      • Robert Dale, Cecile Paris, Marc Tilbrook

      Pages 150-160

   2. Natural Language Agreement Description for Reversible Grammars

      • Stefan Diaconescu

      Pages 161-172

   3. Token Identification Using HMM and PPM Models

      • Yingying Wen, Ian H. Witten, Dianhui Wang

      Pages 173-185

   4. Korean Compound Noun Term Analysis Based on a Chart Parsing Technique

      • Kyongho Min, William H. Wilson, Yoo-Jin Moon

      Pages 186-195

6. Knowledge Discovery and Data Milling II

   1. A Language Modeling Approach to Search Distributed Text Databases

      • Hui Yang, Minjie Zhang

      Pages 196-207

   2. Combining Multiple Host-Based Detectors Using Decision Tree

      • Sang-Jun Han, Sung-Bae Cho

      Pages 208-220

   3. Association Rule Discovery with Unbalanced Class Distributions

      • Lifang Gu, Jiuyong Li, Hongxing He, Graham Williams, Simon Hawkins, Chris Kelman

      Pages 221-232

Consider the problem of a robot (algorithm, learning mechanism) moving along the real line attempting to locate a particular point ? . To assist the me- anism, we assume that it can communicate with an Environment (“Oracle”) which guides it with information regarding the direction in which it should go. If the Environment is deterministic the problem is the “Deterministic Point - cation Problem” which has been studied rather thoroughly [1]. In its pioneering version [1] the problem was presented in the setting that the Environment could charge the robot a cost which was proportional to the distance it was from the point sought for. The question of having multiple communicating robots locate a point on the line has also been studied [1, 2]. In the stochastic version of this problem, we consider the scenario when the learning mechanism attempts to locate a point in an interval with stochastic (i. e. , possibly erroneous) instead of deterministic responses from the environment. Thus when it should really be moving to the “right” it may be advised to move to the “left” and vice versa. Apart from the problem being of importance in its own right, the stoch- tic pointlocationproblemalsohas potentialapplications insolvingoptimization problems. Inmanyoptimizationsolutions–forexampleinimageprocessing,p- tern recognition and neural computing [5, 9, 11, 12, 14, 16, 19], the algorithm worksits wayfromits currentsolutionto the optimalsolutionbasedoninfor- tion that it currentlyhas. A crucialquestionis oneof determining the parameter whichtheoptimizationalgorithmshoulduse.

• Ontologie
• agents
• artificial intelligence
• data mining
• expert system
• knowledge
• knowledge discovery
• learning
• machine learning
• neural network
• problem solving
• proving

• Department of Computer Science, Australian National University, Acton, Australia

  Tamás (Tom) Domonkos Gedeon

• Murdoch University,  

  Lance Chun Che Fung

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