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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 SCREECH Barbara Hammer Computer Science, Clausthal University of Technology, Germany Pascal Hitzler AIFB, Universiy of Karlsruhe, Germany Slide 1
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
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
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
connectionist knowledge representation and reasoning Slide 5
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
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