GWAI-86 und 2. Österreichische Artificial-Intelligence-Tagung

Ottenstein/Niederösterreich, September 22–26, 1986

  • Claus-Rainer Rollinger
  • Werner Horn
Conference proceedings

Part of the Informatik-Fachberichte book series (INFORMATIK, volume 124)

Table of contents

  1. Front Matter
    Pages I-X
  2. AI — NIE! Versuch über eine wahrscheinliche zukünftige Reaktion der Öffentlichkeit

  3. Suchen, Problemlösen

    1. Helmut Horacek, Hermann Kaindl, Marcus Wagner
      Pages 17-27
    2. Alexander Reinefeld
      Pages 28-33
  4. Repräsentation von Wissen

    1. Włodzimierz Zadroƶny
      Pages 34-45
    2. Jürgen Edelmann, Bernd Owsnicki
      Pages 69-74
    3. Bernhard Nebel, Norman K. Sondheimer
      Pages 75-86
  5. Natürlichsprachliche Systeme

  6. Kognitive Prozesse

  7. Maschinelles Lernen

  8. Bildverstehen

  9. Theorembeweisen

    1. Jürgen Müller, Elvira Wagner
      Pages 242-253
    2. Jürgen Müller, Joachim Steinbach
      Pages 254-264
  10. Programmsynthese

  11. KI—Programmierung

    1. Thomas Rose, Hans-Jürgen Appelrath, Hermann Bense
      Pages 301-311
    2. C. Beckstein, G. Görz, M. Tielemann
      Pages 312-317
  12. Expertensysteme

    1. Frank Puppe
      Pages 332-342
    2. Joachim Diederich, Mark May, Ingo Ruhetann
      Pages 343-348

About these proceedings


Decision makinq in larqe domains very often involves the necessity to handle unclear situations. So the ability to base ones decisions on estimates is important in real life as well as in complicated qames. Siqnificantly, even the analysis of chess positions by qrandmasters often results in the conclusion "unclear". The conventional methods in two-person qames (which are by far the most successful ones up to now) use point-values and depth-first (alpha-beta) minimax search (mostly in a brute-force manner). Unfortunately, this approach has a fundamental drawback in unclear situations: it iqnores the uncertainty of the values. Even refinements like quiescence search [4] or extendinq the horizon of the fUll-width search (e.q. by not-countinq certain moves as a ply of depth) [5] cannot completely resolve this defect. Another method proposed by Pearl [7] treats estimated values as probabilities and uses a product propaqation rule. This way the uncertainty of values is qiven too much emphasis and it seems not to be used in practical proqrams. Additionally, this method requires searchinq of the whole tree unlike alpha-beta minimax. Much more convenient for our problem are methods usinq ranqes U, 8] or even probability distributions [6] as values. Unfortunately, they are impracticable for a larqe domain up to now, because of the qreat difficulty in findinq valid bounds (parameters of the distribution). consequently, the converqence of such searches is very hard to quarantee.


Extension Prolog artificial intelligence expert system grammar intelligence knowledge representation learning machine learning natural language natürlichsprachliche Systeme probability problem solving proving uncertainty

Editors and affiliations

  • Claus-Rainer Rollinger
    • 1
  • Werner Horn
    • 2
  1. 1.IBM Deutschland GmbHStuttgart 80Germany
  2. 2.Institut für Medizinische Kybernetik und Artificial IntelligenceUniversität WienWienAustria

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 1986
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-16808-9
  • Online ISBN 978-3-642-71385-9
  • Series Print ISSN 0343-3005
  • Buy this book on publisher's site