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We study the extension of techniques from Inductive Logic Programming (ILP) to temporal logic programming languages. Therefore we present two temporal logic programming languages and analyse the learnability of programs from these languages from finite sets of examples. In first order temporal logic the following topics are analysed: - How can we characterize the denotational semantics of programs? - Which proof techniques are best suited? - How complex is the learning task? In propositional temporal logic we analyse the following topics: - How can we use well known techniques from model checking in order to refine programs? - How complex is the learning task? In both cases we present estimations for the VC-dimension of selected classes of programs.

We propose a framework for the synthesis of temporal logic programs which are formulated in a simple temporal logic programming language from both positive and negative examples. First we will prove that results from the theory of first order inductive logic programming carry over to the domain of temporal logic. After this we will show how programs formulated in the presented language can be generalized or specialized in order to satisfy the specification induced by the sets of examples.

We propose several algorithms for efficient Testing of logical Implication in the case of ground objects. Because the problem of Testing a set of propositional formulas for (un)satisfiability is \(NP\)-complete there's strong evidence that there exist examples for which every algorithm which solves the problem of testing for (un)satisfiability has a runtime that is exponential in the length of the input. So will have our algorithms. We will therefore point out classes of logic programs for which our algorithms have a lower runtime. At the end of this paper we will give an outline of an algorithm for theory refinement which is based on the algorithms described above.