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Optimal strategies — Learning from examples — Boolean equations

  • Christian Posthoff
  • Michael Schlosser
2 Inductive Inference for Artificial Intelligence 2.1 Theoretical Approaches
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 961)

Abstract

The paper covers a wide range of knowledge acquisition, knowledge engineering and Machine Learning. The main idea is to unify a lot of AI problems using set-theoretic concepts and logical functions.

Many concepts of knowledge-based problem-solving are incorporated into one system which has been based on set-theoretical concepts. This results in a consisting methodology and in a comprehensive set of tools applicable in many fields. The transition between fuzzy and non-fuzzy parts of the problem domain can be realized very flexible supplying a high smartness and a high performance of the constructed model.

Part A of the paper shows the basis for different Machine Learning methods using logical equations. Part B gives a way how to use optimal strategies in order to obtain complete knowledge. A few examples illustrate the method.

Keywords

Logical Equation Disjunctive Normal Form Binary Attribute Fuzzy Equivalence Relation Boolean Equation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Christian Posthoff
    • 1
  • Michael Schlosser
    • 2
  1. 1.Dept. of Mathematics and Computer ScienceUniversity of The West IndiesSt. AugustineTrinidad & Tobago
  2. 2.Fakultät für InformatikTechnische Universität Chemnitz-ZwickauChemnitzGermany

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