Making believers out of computers
Artificial Intelligence
Some results concerning the computational complexity of abduction
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Structure identification in relational data
Artificial Intelligence - Special volume on constraint-based reasoning
Tractable default reasoning
Horn approximations of empirical data
Artificial Intelligence
Stochastic search and phase transitions: AI meets physics
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
The complexity of theory revision
Artificial Intelligence
First order LUB approximations: characterization and algorithms
Artificial Intelligence - Special volume on reformulation
A connectionist framework for reasoning: reasoning with examples
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
On the Boolean connectivity problem for Horn relations
Discrete Applied Mathematics
Deductive inference for the interiors and exteriors of horn theories
ACM Transactions on Computational Logic (TOCL)
DNF hypotheses in explanatory induction
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Detecting mistakes in binary data tables
Automatic Documentation and Mathematical Linguistics
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Formal AI systems traditionally represent knowledge using logical formulas. We will show, however, that for certain kinds of information, a model-based representation is more compact and enables faster reasoning than the corresponding formula-based representation. The central idea behind our work is to represent a large set of models by a subset of characteristic models. More specifically, we examine model-based representations of Horn theories, and show that there are large Horn theories that can be exactly represented by an exponentially smaller set of characteristic models. In addition, we will show that deduction based on a set of characteristic models takes only linear time, thus matching the performance using Horn, theories. More surprisingly, abduction can be performed in polynomial time using a set of characteristic models, whereas abduction using Horn theories is NP-complete.