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Separating Easy and Hard Logic Programming Problems Background: When we generate random instances of NP-Complete problems, depending upon what random distributions we use, problems may be usually hard, usually easy, or mixed (that is both hard and easy). Most famous is probably the research, started by Franco and Paul, about when random a randomly generated k-CNF formula is probably satisfiable. Problem Statement: A less studied, and apparently more complicated problem is the distribution of normal logic programs which have "stable model". Stable models grew out of "Artificial Intelligence (AI) and programming language called Prolog". Stable models are also describe briefly in the text used in "AI 1 and 2" this and last year. We will use existing software to test for the existence of stable models. Studying stable models is desirable because there are some NP problems shich are much easier to reduce to the existence of stable models than to propositional satisfiability Team Members: Olatunde Philip Adejumobi mojeboski2006@yahoo.com Jeremy Lavergne lavergje@email.uc.edu Andrew Fickas fickasaj@email.uc.edu Faculty Advisor: Professor John Schlipf (Computer Science at University of Cincinnati) and Professor M. Truszcznski (University of Kentucky) Goal: Is to run experiments that will tell us where and how to search for stable models for some class of logic programming. Also, for the project to serves as guidiance for future work in this area of theory of computation (that is, possibly write a publishable paper). Subgoals:
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