Martin Hofmann

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In this paper we present a comparison of several inductive programming (IP) systems. IP addresses the problem of learning (recursive) programs from incomplete specifications, such as input/output examples. First, we introduce conditional higher-order term rewriting as a common framework for induc-tive program synthesis. Then we characterise the ILP system(More)
In this paper we present a comparison of several inductive programming (IP) systems. IP addresses the problem of learning (recursive) programs from incomplete specifications, such as input/output examples. First, we introduce conditional higher-order term rewriting as a common framework for inductive logic and inductive functional program synthesis. Then we(More)
Inductive programming (IP), usually defined as a search in a space of candidate programs, is an inherent exponentially complex problem. To constrain the search space, program templates have ever been one of the first choices. In previous approaches to incorporate program schemes, either an (often very well) informed expert user has to provide a template in(More)
Analytical inductive programming and evolutionary in-ductive programming are two opposing strategies for learning recursive programs from incomplete specifications such as input/output examples. Analytical induc-tive programming is data-driven, namely, the minimal recursive generalization over the positive input/output examples is generated by recurrence(More)
One of the most admirable characteristic of the human cognitive system is its ability to extract generalized rules covering regularities from example experience presented by or experienced from the environment. Humans' problem solving, reasoning and verbal behavior often shows a high degree of systematicity and productivity which can best be characterized(More)