Andreas Witzel

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We propose an abstract approach to coalition formation that focuses on simple merge and split rules transforming partitions of a group of players. We identify conditions under which every iteration of these rules yields a unique partition. The main conceptual tool is a specific notion of a stable partition. The results are parametrized by a preference(More)
Significant advances have recently been made concerning the integration of symbolic knowledge representation with artificial neural networks (also called connectionist systems). However, while the integration with propositional paradigms has resulted in applicable systems, the case of first-order knowledge representation has so far hardly proceeded beyond(More)
1 Institute for Logic, Language and Computation, Universiteit van Amsterdam, Postbus 94242, 1090 GE Amsterdam, The Netherlands; 2 Department Mathematik, Universität Hamburg, Bundesstrasse 55, 20146 Hamburg, Germany 3 Mathematisches Institut, Rheinische Friedrich-Wilhelms-Universität Bonn, Endenicher Allee 60, 53115 Bonn, Germany 4 Tilburg(More)
The field of neural-symbolic integration has received much attention recently. While with propositional paradigms, the integration of symbolic knowledge and connectionist systems (also called artificial neural networks) has already resulted in applicable systems, the theoretical foundations for the first-order case are currently being laid and first(More)
We present a fully connectionist system for the learning of first-order logic programs and the generation of corresponding models: Given a program and a set of training examples, we embed the associated semantic operator into a feed-forward network and train the network using the examples. This results in the learning of first-order knowledge while damaged(More)
The main aim of this paper is to raise awareness of higherorder knowledge (knowledge about someone else’s knowledge) as an issue for computer game AI. We argue that a number of existing game genres, especially those involving social interaction, are natural fields of application for an approach we call explicit knowledge programming. We motivate the use of(More)