PRICAI 2000 Topics in Artificial Intelligence

1. Front Matter

   Pages I-XX

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2. Invited Talks

   1. Automated Haggling: Building Artificial Negotiators

      • Nick Jennings

      Pages 1-1

   2. Information Geometry of Neural Networks

      • Shun-ichi Amari

      Pages 2-2

   3. Knowledge Representation, Belief Revision, and the Challenge of Optimality

      • Randy Goebel

      Pages 3-3

   4. Artificial Intelligence Applications in Electronic Commerce

      • Jae Kyu Lee

      Pages 4-4

   5. Towards a Next-Generation Search Engine

      • Qiang Yang, Hai-Feng Wang, Ji-Rong Wen, Gao Zhang, Ye Lu, Kai-Fu Lee et al.

      Pages 5-15

3. Logic and Foundations of AI

   1. The Gap between Symbol and Non-symbol Processing

      • Setsuo Ohsuga

      Pages 16-26

   2. Argumentation Semantics for Defeasible Logics

      • G. Governatori, M. J. Maher, G. Antoniou, D. Billington

      Pages 27-37

   3. A Unifying Semantics for Causal Ramifications

      • Mikhail Prokopenko, Maurice Pagnucco, Pavlos Peppas, Abhaya Nayak

      Pages 38-49

   4. Inconsistency and Preservation

      • Paul Wong

      Pages 50-60

4. Induction and Logic Programming

   1. Inductive Inference of Chess Player Strategy

      • Anthony R. Jansen, David L. Dowe, Graham E. Farr

      Pages 61-71

   2. Compiling Logical Features into Specialized State-Evaluators by Partial Evaluation, Boolean Tables and Incremental Calculation

      • Tomoyuki Kaneko, Kazunori Yamaguchi, Satoru Kawai

      Pages 72-82

   3. Using Domain Knowledge in ILP to Discover Protein Functional Models

      • Takashi Ishikawa, Masayuki Numao, Takao Terano

      Pages 83-92

   4. The Hyper System: Knowledge Reformation for Efficient First-Order Hypothetical Reasoning

      • Helmut Prendinger, Mitsuru Ishizuka, Tetsu Yamamoto

      Pages 93-103

   5. Determination of General Concept in Learning Default Rules

      • Kouzou Ohara, Hideyuki Taka, Noboru Babaguchi, Tadahiro Kitahashi

      Pages 104-114

5. Reinforcement Learning

   1. A Theory of Profit Sharing in Dynamic Environment

      • Shingo Kato, Hiroshi Matsuo

      Pages 115-124

   2. Experience-Based Reinforcement Learning to Acquire Effective Behavior in a Multi-agent Domain

      • Sachiyo Arai, Katia Sycara, Terry R. Payne

      Pages 125-135

   3. A Region Selecting Method Which Performs Observation and Action in the Multi-resolution Environment

      • Toshihiro Matsui, Hiroshi Matsuo, Akira Iwata

      Pages 136-145

6. Machine Learning

   1. RWS (Random Walk Splitting): A Random Walk Based Discretization of Continuous Attributes

      • Masaaki Hanaoka, Masaki Kobayashi, Haruaki Yamazaki

      Pages 146-155

   2. The Lumberjack Algorithm for Learning Linked Decision Forests

      • William T. B. Uther, Manuela M. Veloso

      Pages 156-166

PRICAI 2000, held in Melbourne, Australia, is the sixth Pacific Rim Interna­ tional Conference on Artificial Intelligence and is the successor to the five earlier PRICAIs held in Nagoya (Japan), Seoul (Korea), Beijing (China), Cairns (Aus­ tralia) and Singapore in the years 1990, 1992, 1994, 1996 and 1998 respectively. PRICAI is the leading conference in the Pacific Rim region for the presenta­ tion of research in Artificial Intelligence, including its applications to problems of social and economic importance. The objectives of PRICAI are: To provide a forum for the introduction and discussion of new research results, concepts and technologies; To provide practising engineers with exposure to and an evaluation of evolving research, tools and practices; To provide the research community with exposure to the problems of practical applications of AI; and To encourage the exchange of AI technologies and experience within the Pacific Rim countries. PRICAI 2000 is a memorial event in the sense that it is the last one in the 20"" century. It reflects what researchers in this region believe to be promising for their future AI research activities. In fact, some salient features can be seen in the papers accepted. We have 12 papers on agents, while PRICAI 96 and 98 had no more than two or three. This suggests to us one of the directions in which AI research is going in the next century. It is true that agent research provides us with a wide range of research subjects from basic ones to applications.

• AI logics
• agent technology
• algorithmic learning
• artificial intelligence
• autonomous robot
• constraint satisfaction problem
• genetic programming
• heuristics
• intelligent agents
• intelligent system
• knowledge discovery
• knowledge processing
• natural language processing
• optimization
• supervised learning

• Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan

  Riichiro Mizoguchi

• Computer Sciences Laboratory, Research School of Information Sciences and Engineering, Australian National University, Canberra, Australia

  John Slaney

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