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