DMCA
Reconfigurable Hardware Accelerator for Intelligent Software Agents
BibTeX
@MISC{Grantner_reconfigurablehardware,
author = {Janos L. Grantner and Paolo A. Tamayo and Ramakrishna Gottipati and George A. Fodor},
title = {Reconfigurable Hardware Accelerator for Intelligent Software Agents},
year = {}
}
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Abstract
Programs for contemporary industrial control systems are designed using Object Oriented methods and software agents. It is required that the system should reach its objectives even when unexpected events occur in an uncertain environment. There is need for a high-level information representation such that a predictable behavior can be achieved in a uniform way for all agents. A fuzzy automaton-based approach offers clear benefits for developing agents of reconfigurable architecture. Some of the most difficult problems in industrial process control innovation are as follows: how can new knowledge be introduced to the system, how can the system activate stored domain knowledge in an autonomous way, how can the knowledge be validated (or recognized as invalid), and how can the system recover if the new activated knowledge (or the currently active knowledge) is not suitable to handle the current situation of the plant. A fuzzy automaton can implement new knowledge by means of the states of the goal path of an event-driven, sequential control algorithm while providing an effective approximation method to model continuous and discrete signals in a single theoretical framework. With respect to problem that how can the knowledge be validated
Keyphrases
intelligent software agent reconfigurable hardware accelerator new knowledge object oriented method autonomous way fuzzy automaton contemporary industrial control system reconfigurable architecture industrial process control innovation system activate unexpected event active knowledge discrete signal uncertain environment software agent predictable behavior sequential control algorithm single theoretical framework high-level information representation goal path domain knowledge uniform way difficult problem current situation effective approximation method
