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The \($\theta$\)-subsumption problem is crucial to the efficiency of ILP learning systems. We discuss two \($\theta$\)-subsumption algorithms based on strategies for preselecting suitable matching literals. The class of clauses, for which subsumption becomes polynomial, is a superset of the deterministic clauses. We further map the general problem of \($\theta$\)-subsumption to a certain problem of finding a clique of fixed size in a graph, and in return show that a specialization of the pruning strategy of the Carraghan and Pardalos clique algorithm provides a dramatic reduction of the subsumption search space. We also present empirical results for the mesh design data set.
Artificial Intelligence and Sustainability
Our research group investigates both the use of energy in developing artificial intelligence (AI) as well as the use of AI in generating, storing and managing energy. This includes research into energy-efficient algorithms for solving basic AI tasks such as classification, ranking or planning & search, as well as the development and application of AI methods to refined modeling of batteries in order to extend their working lifetime, and the control of domestic energy consumption.