A blackboard architecture for control
Artificial Intelligence
Control knowledge in expert systems: relaxing restrictive assumptions
5th International Workshop Vol. 2 on Expert systems & their applications
PLAKON—an approach to domain-independent construction
IEA/AIE '89 Proceedings of the 2nd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Specifying Meta-Level Architectures for Rule-Based Systems
GWAI '87 Proceedings of the 11th German Workshop on Artificial Intelligence
Configuration Using PLAKON - An Applications Perspective
Wissensbasierte Systeme, 3. Internationaler GI-Kongress
Representing Control Knowledge as Abstract Task and Metarules
Representing Control Knowledge as Abstract Task and Metarules
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In most expert systems for constructional tasks the knowledge base consists of a set of facts or object definitions and a set of rules. These rules contain knowledge about correct or ideal solutions as well as knowledge on how to control the construction process. In this paper we present an approach that avoids this type of rules and thus the disadvantages caused by them.We propose a static knowledge base consisting of a set of object definitions interconnected by is-a and part-of links. This conceptual hierarchy declaratively defines a taxonomy of domain objects and the aggregation of components to composite objects. Thus, the conceptual hierarchy describes the set of all admissible solutions to a constructional problem. Interdependencies between objects are represented by constraints. A solution is a syntactically complete and correct partial instantiation of the conceptual hierarchy.No control knowledge is included in the conceptual hierarchy. Instead, the control mechanism will use it as a guideline. It is thus possible to determine in which respects a current partial solution is incomplete, simply by comparing it with the conceptual hierarchy syntactically. The most important advantage of this approach is the ability to represent control knowledge and structural knowledge separately.