In this paper, we describe recent attempts to incorporate learning into logic programs as a step toward adaptive software that can learn from an environment. Although there are a variety of types of learning, we focus on parameter learning of logic programs, one for statistical learning by the EM algorithm and the other for reinforcement learning by learning automatons. Both attempts are not full-fledged yet, but in the former case, thanks to the general framework and an efficient EM learning algorithm combined with a tabulated search, we have obtained very promising results that open up the prospect of modeling complex symbolic-statistical phenomena.
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