Abstract Learning Frameworks for Synthesis

作者: Christof Löding , P. Madhusudan , Daniel Neider

DOI: 10.1007/978-3-662-49674-9_10

关键词: Sample spaceSemantics (computer science)Convergent synthesisoccamVariety (cybernetics)Theoretical computer scienceCounterexampleSpace (commercial competition)Artificial intelligenceMathematicsIterative learning control

摘要: We develop abstract learning frameworks for synthesis that embody the principles of CEGIS counterexample-guided inductive algorithms in current literature. Our framework is based on iterative from a hypothesis space captures synthesized objects, using counterexamples an sample space, and concept abstractly defines semantics synthesis. show variety literature can be embedded this general framework. also exhibit three recipes convergent synthesis: first two finite spaces Occam learners generalize all techniques convergence used existing engines, while third, involving well-founded quasi-orderings, new, we instantiate it to concrete problems.

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