Publikation
Task Acquisition with a Description Logic Reasoner
Martin Buchheit; Hans-Jürgen Bürckert; Bernhard Hollunder; Armin Laux; Werner Nutt; Marek Wójcik
In: Ipke Wachsmuth; Claus-Rainer Rollinger; Wilfried Brauer (Hrsg.). KI'95: Proceedings of the 19th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence. German Conference on Artificial Intelligence (KI), Pages 125-136, Lecture Notes In Computer Science (LNCS), Vol. 981, ISBN 3-540-60343-3, Springer-Verlag, London, UK, 1995.
Zusammenfassung
In many knowledge based systems the application domain is modeled in an object-centered formalism. Research in knowledge acquisition has given evidence that this approach allows one to adequately model the conceptual structures of human experts. However, when a novice user wants to describe a particular task to be solved by such a system he has to be well acquainted with the underlying domain model, and therefore is charged with the burden of making himself familiar with it. We aim at giving automated support to a user in this process, which we call task acquisition.
This paper describes the Tacos system, which guides a user through an object-centered domain model and gives support to him in specifying his task. A characteristic of Tacos is that the user can enter only information that is meaningful and consistent with the domain model. In order to identify such information, Tacos exploits the ability of a description logic based knowledge representation system to reason about such models.
This paper describes the Tacos system, which guides a user through an object-centered domain model and gives support to him in specifying his task. A characteristic of Tacos is that the user can enter only information that is meaningful and consistent with the domain model. In order to identify such information, Tacos exploits the ability of a description logic based knowledge representation system to reason about such models.