Real-time AI systems: A definition and an architecture

Real-time AI systems: A definition and an architecture (1989)

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by Rajendra Dodhiawala , N. S. Sridharan , Peter Raulefs , Cynthia Pickering

Venue:	In Proceedings of the International Joint Conference on Artificial Intelligence
Citations:	10 - 0 self

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Datum	Value	Source
TITLE	Real-time AI systems: A definition and an architecture	INFERENCE
AUTHOR NAME	Rajendra Dodhiawala	SVM HeaderParse 0.2
AUTHOR AFFIL	FMC Corporate Technology Center; 1205 Coleman Dr., Box 580, Santa Clara	SVM HeaderParse 0.2
AUTHOR ADDR	California 95052	SVM HeaderParse 0.2
AUTHOR NAME	N. S. Sridharan	SVM HeaderParse 0.2
AUTHOR AFFIL	FMC Corporate Technology Center; 1205 Coleman Dr., Box 580, Santa Clara	SVM HeaderParse 0.2
AUTHOR ADDR	California 95052	SVM HeaderParse 0.2
AUTHOR NAME	Peter Raulefs	SVM HeaderParse 0.2
AUTHOR AFFIL	FMC Corporate Technology Center; 1205 Coleman Dr., Box 580, Santa Clara	SVM HeaderParse 0.2
AUTHOR ADDR	California 95052	SVM HeaderParse 0.2
AUTHOR NAME	Cynthia Pickering	SVM HeaderParse 0.2
AUTHOR AFFIL	FMC Corporate Technology Center; 1205 Coleman Dr., Box 580, Santa Clara	SVM HeaderParse 0.2
AUTHOR ADDR	California 95052	SVM HeaderParse 0.2
ABSTRACT	Speed alone is insufficient for real-time performance. We define real-time performance in terms of speed, responsiveness, timeliness, and graceful adaptation. We claim that all four aspects are essential if a system is to support real-time problem-solving. We palso present a distributed knowledge rocessing architecture based on the lackboard paradigm that addresses all aspects of real-time performance. Primary attention was given to flexibility of behavior without compromising on the efficiency of implementation so that the applicability of the architecture to an application may be experimented with. Performance metrics are crucial for validating real-time performance, and form an integral component of a real-time system. In this paper, we present performance metrics for responsiveness and timeliness at the architecture level. 1.	SVM HeaderParse 0.2
YEAR	1989	INFERENCE
VENUE	In Proceedings of the International Joint Conference on Artificial Intelligence	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	256--261	INFERENCE
VOLUME	16	INFERENCE
CITATIONS	12 found	ParsCit 1.0