Connectionist Knowledge Representation and Reasoning

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