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Connectionist Knowledge Representation and Reasoning

Connectionist Knowledge Representation and Reasoning. SCREECH. Barbara Hammer Computer Science, Clausthal University of Technology, Germany Pascal Hitzler AIFB, Universiy of Karlsruhe, Germany. General Motivation. connectionist. knowledge representation and reasoning.

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Connectionist Knowledge Representation and Reasoning

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  1. Connectionist Knowledge Representation and Reasoning SCREECH Barbara Hammer Computer Science, Clausthal University of Technology, Germany Pascal Hitzler AIFB, Universiy of Karlsruhe, Germany Slide 1

  2. General Motivation connectionist knowledge representation and reasoning • Artificial Neural Networks and Symbolic Knowledge Representation and Reasoning are two diverse Paradigms in Artificial Intelligence. • Their strengths and weaknesses complement each other. • We seek to combine them in order to obtain systems with functionalities being the best of both worlds. Slide 2

  3. Artificial Neural Networks (ANNs) • Powerful machine learning paradigm. • Architectures inspired by Biology. • Can be trained on raw and noisy data. • Robust. Graceful degradation. • No declarative reading. Black boxes. • Dealing with recursive structures difficult. • Training cannot take a priori domain knowledge into account.   Slide 3

  4. Knowledge Representation and Reasoning (KRR) • Logic-based. Declarative. • Modelling inspired by human thinking. • Simple manual coding of knowledge. • Highly recursive. • Systems hard to train. • No tolerance to noise. Brittle. • Reasoning algorithms with high complexities.   Slide 4

  5. connectionist knowledge representation and reasoning     Slide 5

  6. Issues in Connectionist KRR • Representation of symbolic knowledge within ANNs. • Extraction of symbolic knowledge from ANNs. • Learning of symbolic knowledge using ANNs. • Learning taking symbolic background knowledge into account. Slide 6

  7. Tutorial Outline • Part I: Neural networks and structured knowledge • Feedforward networks • Recurrent networks • Recursive data structures • Part II: Logic and neural networks • Propositional logic • First-order logic • Future challenges Slide 7

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