The WASP Showcase-Collection

WASP (IST project IST-FET-2001-37004)
Official Project-Website

Answer-Set Programming

The representation and manipulation of knowledge is one of the central tasks underlying the realization of intelligent systems. One access to this area is the use of declarative approaches, in which knowledge is represented as sentences in symbolic languages and inference rules access these sentences for the process of finding conclusions from a given set of premises. The Answer-Set Programming approach is characterized by the feature that problems are represented in terms of logic programs such that the models of the latter determine the solutions of the original problem:

Fig.1: General schema for problem encodings

More specifically, to encode the general features of the tackled problem, one uses a common program, i.e. a set of inference-rules with variables, abstracting a concrete instances of the problem, at hand. The instance is then fed into this program as a set of facts (like a database):

Fig.2: Schema for problem encodings with a separation between specification and data

Answer-set Programming is thus in contrast to knowledge representation as done by means of formal logics, in which a proof constitutes an answer to a reasoning problem. The strength and distinguishing feature of Answer-Set Programming hereby is its advanced capability of dealing with incomplete information and defaults, basically provided by nonmonotonic negation.

Application Areas

Answer-Set Programming has been successfully applied to many areas including

(see the WP-5 Survey for an exhaustive collection of such applications and pointers to the literature).

In what follows, we provide links to some selected model applications, which are provided as demos by different WASP-nodes.

Selected Model Applications