The Blackboard Architecture in Knowledge-Based Robotic Systems

Abstract

Integrated knowledge-based robotic systems that employ multi-sensor feedback information require the effective treatment of a large volume of complex knowledge. To this end, several methods of knowledge organisation and exploitation have been developed. One of them is the so-called blackboard architecture, which belongs to the class of distributed problem solving architectures, and employs more than one problem solving agents. The three basic elements of the blackboard architecture are: the blackboard, the knowledge sources and the control mechanism. The blackboard plays the role of a central working memory within the system and the controller stands for the conflict resolution mechanism. The knowledge about the system (problem) is split in a number of comparatively small knowledge bases called knowledge sources and controlled through the blackboard control (scheduling) mechanism which is usually a meta-knowledge control mechanism. This mechanism is not committed to any reasoning technique (e.g. forward or backward inference chaining), but has a rather opportunistic nature (i.e., the control action is determined by the optimal decision taken at each cycle of operation). The purpose of this paper is to provide an account of the main concepts and issues of the blackboard architecture and their use in designing integrated knowledge-based intelligent robotic systems. Some particular applications of this approach will also be discussed, one of which has been designed and tested by the authors. The paper is complemented by general design issues of intelligent robotic and flexible manufacturing systems.