Parameter and Structure Learning Algorithms for Statistical Relational Learning

نویسندگان

  • Elena Bellodi
  • Fabrizio Riguzzi
چکیده

My research activity focuses on the field of Machine Learning. Two key challenges in most machine learning applications are uncertainty and complexity. The standard framework for handling uncertainty is probability, for complexity is first-order logic. Thus we would like to be able to learn and perform inference in representation languages that combine the two. This is the focus of the field of Statistical Relational Learning. My research is based on the use of the vast plethora of techniques developed in the field of Logic Programming, in which the distribution semantics [16] is one of the most prominent approaches. This semantics underlies, e.g., Probabilistic Logic Programs,Probabilistic Horn Abduction,PRISM [16], Independent Choice Logic,pD,Logic Programs with Annotated Disjunctions (LPADs) [17], ProbLog [5] and CP-logic. These languages have the same expressive power: there are linear transformations from one to the others. LPADs offer the most general syntax, so my research and experimentations has been focused on this formalism. An LPAD consists of a finite set of disjunctive clauses, where each of the disjuncts in the head of a clause is annotated with the probability of the disjunct to hold, if the body of the clause holds. LPADs are particularly suitable when reasoning about actions and effects where we have causal independence among the possible different outcomes for a given action. Various works have appeared for solving three types of problems for languages under the distribution semantics:

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تاریخ انتشار 2012