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Constraint satisfaction for inductive logic programming
 Chovanec, Andrej ; Barták, Roman (advisor) ; Železný, Filip (referee)
Inductive logic programming is a discipline investigating invention of clausal theories from observed examples such that for given evidence and background knowledge we are finding a hypothesis covering all positive examples and excluding all negative ones. In this thesis we extend an existing work on template consistency to general consistency. We present a three-phase algorithm DeMeR decomposing the original problem into smaller subtasks, merging all subsolutions together yielding a complete solution and finally refining the result in order to get a compact final hypothesis. Furthermore, we focus on a method how each individual subtask is solved and we introduce a generate-and-test method based on the probabilistic history-driven approach for this purpose. We analyze each stage of the proposed algorithms and demonstrate its impact on a runtime and a hypothesis structure. In particular, we show that the first phase of the algorithm concentrates on solving the problem quickly at the cost of longer solutions whereas the other phases refine these solutions into an admissible form. Finally, we prove that our technique outperforms other algorithms by comparing its results for identifying common structures in random graphs to existing systems.
Detailed record
Constraint satisfaction for inductive logic programming
 Chovanec, Andrej ; Barták, Roman (advisor) ; Železný, Filip (referee)
Inductive logic programming is a discipline investigating invention of clausal theories from observed examples such that for given evidence and background knowledge we are finding a hypothesis covering all positive examples and excluding all negative ones. In this thesis we extend an existing work on template consistency to general consistency. We present a three-phase algorithm DeMeR decomposing the original problem into smaller subtasks, merging all subsolutions together yielding a complete solution and finally refining the result in order to get a compact final hypothesis. Furthermore, we focus on a method how each individual subtask is solved and we introduce a generate-and-test method based on the probabilistic history-driven approach for this purpose. We analyze each stage of the proposed algorithms and demonstrate its impact on a runtime and a hypothesis structure. In particular, we show that the first phase of the algorithm concentrates on solving the problem quickly at the cost of longer solutions whereas the other phases refine these solutions into an admissible form. Finally, we prove that our technique outperforms other algorithms by comparing its results for identifying common structures in random graphs to existing systems.
Detailed record

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