AI 2003: Advances in Artificial Intelligence

16th Australian Conference on AI, Perth, Australia, December 3-5, 2003. Proceedings

  • Tamás (Tom) Domonkos Gedeon
  • Lance Chun Che Fung
Conference proceedings AI 2003

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2903)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2903)

Table of contents

  1. Front Matter
  2. Keynote Papers

    1. Kotagiri Ramamohanarao, James Bailey
      Pages 1-11
    2. B. John Oommen, Govindachari Raghunath, Benjamin Kuipers
      Pages 24-40
    3. Mohan S. Kankanhalli
      Pages 41-52
  3. Ontology

    1. Insu Song, Pushkar Piggott
      Pages 53-64
    2. Seung Yeol Yoo, Achim Hoffmann
      Pages 65-76
    3. Hyunjang Kong, Kwanho Jung, Junho Choi, Wonpil Kim, Pankoo Kim, Jongan Park
      Pages 77-87
  4. Problem Solving

    1. A. Anbulagan, John Thornton, Abdul Sattar
      Pages 100-111
    2. Atsuko Mutoh, Tsuyoshi Nakamura, Shohei Kato, Hidenori Itoh
      Pages 112-124
    3. Ying Kong, Fan Wang, Andrew Lim, Songshan Guo
      Pages 125-136
    4. Wayne Pullan, Liang Zhao, John Thornton
      Pages 137-149
  5. Knowledge Discovery and Data Mining I

    1. Robert Dale, Cecile Paris, Marc Tilbrook
      Pages 150-160
    2. Yingying Wen, Ian H. Witten, Dianhui Wang
      Pages 173-185
    3. Kyongho Min, William H. Wilson, Yoo-Jin Moon
      Pages 186-195
  6. Knowledge Discovery and Data Milling II

    1. Sang-Jun Han, Sung-Bae Cho
      Pages 208-220
    2. Lifang Gu, Jiuyong Li, Hongxing He, Graham Williams, Simon Hawkins, Chris Kelman
      Pages 221-232
    3. Yudho Giri Sucahyo, Raj P. Gopalan, Amit Rudra
      Pages 233-244
  7. Expert Systems

    1. Elpiniki Papageorgiou, Chrysostomos Stylios, Peter Groumpos
      Pages 256-268
    2. Xizhao Wang, Minghua Zhao, Dianhui Wang
      Pages 282-292
  8. Neural Networks Applications

    1. Vishy Karri, Tossapol Kiatcharoenpol
      Pages 293-301
    2. Jason Teo, Hussein A. Abbass
      Pages 302-314
    3. Jun Kong, D. G. Li, A. C. Watson
      Pages 315-326
  9. Belief Revisioii and Theorem Proving

    1. Guido Governatori, Alessio Lomuscio, Marek J. Sergot
      Pages 339-351
    2. Samir Chopra, Johannes Heidema, Thomas Meyer
      Pages 364-376
    3. Dongmo Zhang, Norman Foo
      Pages 377-389
  10. Reasoning and Logic

    1. Remco R. Bouckaert
      Pages 390-401
    2. Katarina Britz, Johannes Heidema
      Pages 402-413
    3. Guido Governatori, Vineet Padmanabhan
      Pages 414-426
    4. Lingzhong Zhou, John Thornton, Abdul Sattar
      Pages 427-439
  11. Machine Learning I

    1. Ying Yang, Geoffrey I. Webb
      Pages 440-452
    2. Zhihai Wang, Geoffrey I. Webb, Fei Zheng
      Pages 453-465
  12. AI Applications

    1. Fan Yang, Peng Han, Ruimin Shen, Bernd J. Kraemer, Xinwei Fan
      Pages 490-500
    2. Peter Smet, Greg Calbert, Jason Scholz, Don Gossink, Hing-Wah Kwok, Michael Webb
      Pages 501-510
    3. Yang Sok Kim, Sung Sik Park, Byeong Ho Kang, Joa Sang Lim
      Pages 511-519
  13. Neural Networks

  14. Intelligent Agents

    1. Eun-Kyung Yun, Sung-Bae Cho
      Pages 578-589

About these proceedings


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

Editors and affiliations

  • Tamás (Tom) Domonkos Gedeon
    • 1
  • Lance Chun Che Fung
    • 2
  1. 1.Department of Computer ScienceAustralian National UniversityActonAustralia
  2. 2.Murdoch University 

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-20646-0
  • Online ISBN 978-3-540-24581-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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