CHARACTERIZING KNOWLEDGE DEPTH IN INTELLIGENT SAFETY SYSTEMS
作者:
TIM FININ,
DAVID KLEIN,
期刊:
Applied Artificial Intelligence
(Taylor Available online 1989)
卷期:
Volume 3,
issue 2-3
页码: 129-142
ISSN:0883-9514
年代: 1989
DOI:10.1080/08839518908949921
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Intelligent process control may be viewed as encompassing four major tasks. An intelligent agent must monitor the target system to obtain the values of relevant stale variables in order to detect problems and to ascertain the status of the components that may be employed in responding to those problems. An intelligent agent must determine plans for managing the current situation. An intelligent agent must select a response (the “best” one) through a process of plan evaluation. Finally, to carry out the chosen response, the agent must perform plan execution. While monitoring and execution are relatively straightforward operations, plan determination and plan evaluation may be accomplished in a number of ways that vary in their relative depth of reasoning. In this paper we sketch an analysis for the reasoning underlying plan determination and evaluation tasks for a class of intelligent control systems that attempt to “provide a safety function.” This analysis has two objectives: to illustrate a domain-independent mode of analysis for examining progressively deeper models, and to make the analysis available to those interested in building systems that provide safety functions.
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