Abstract
Answer Set Programming (ASP) has become a popular approach to declarative problem solving. More precisely, ASP is a rule-based formalism for modeling and solving knowledge-intense combinatorial (optimization) problems. What makes ASP attractive is its combination of a declarative modeling language with highly effective solving engines. This allows us to concentrate on specifying - rather than programming the algorithm for solving - a problem at hand. Historically, ASP has its roots in deductive databases, logic programming, and non-monotonic reasoning; its solving engines draw on the same technology as solvers for satisfiability testing. Given this origin, ASP is tailored to support closed as well as open world reasoning, which makes it predestined for knowledge representation and reasoning tasks. Interesting applications of ASP can be found in decision support systems, industrial team-building, music composition, train scheduling, product and software configuration, phylogeneticics, robotics, systems biology, timetabling, and many more.
The talk will give a gentle introduction to ASP, its logical foundations, modeling capabilities, and solving engines, and conclude with an outlook on the ASP's potential impact as a knowledge-driven AI tool.