Dynamically ordered probabilistic choice logic programming

Marina De Vos, Dirk Vermeir

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)


We present a framework for decision making under uncertainty where the priorities of the alternatives can depend on the situation at hand. We design a logic-programming language, DOP-CLP, that allows the user to specify the static priority of each rule and to declare, dynamically, all the alternatives for the decisions that have to be made. In this paper we focus on a semantics that reflects all possible situations in which the decision maker takes the most rational, possibly probabilistic, decisions given the circumstances. Our model theory, which is a generalization of classical logic-programming model theory, captures uncertainty at the level of total Herbrand interpretations.We also demonstrate that DOP-CLPs can be used to formulate game theoretic concepts.

Original languageEnglish
Pages (from-to)227-239
Number of pages13
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Early online date24 Nov 2000
Publication statusPublished - 24 Nov 2000

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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