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Farah, Jeffrey J.

Publications and source records attributed to Farah, Jeffrey J..

The planning coordinator: A design architecture for autonomous error recovery and on-line planning of intelligent tasks

Developing a robust, task level, error recovery and on-line planning architecture is an open research area. There is previously published work on both error recovery and on-line planning; however, none incorporates error recovery and on-line planning into one integrated platform. The integration of these two functionalities requires an architecture that possesses the following characteristics. The architecture must provide for the inclusion of new information without the destruction of existing information. The architecture must provide for the relating of pieces of information, old and new, to one another in a non-trivial rather than trivial manner (e.g., object one is related to object two under the following constraints, versus, yes, they are related; no, they are not related). Finally, the architecture must be not only a stand alone architecture, but also one that can be easily integrated as a supplement to some existing architecture. This thesis proposal addresses architectural development. Its intent is to integrate error recovery and on-line planning onto a single, integrated, multi-processor platform. This intelligent x-autonomous platform, called the Planning Coordinator, will be used initially to supplement existing x-autonomous systems and eventually replace them.

Farah, Jeffrey J.↗

Utilizing semantic networks to database and retrieve generalized stochastic colored Petri nets

Previous work has introduced the Planning Coordinator (PCOORD), a coordinator functioning within the hierarchy of the Intelligent Machine Mode. Within the structure of the Planning Coordinator resides the Primitive Structure Database (PSDB) functioning to provide the primitive structures utilized by the Planning Coordinator in the establishing of error recovery or on-line path plans. This report further explores the Primitive Structure Database and establishes the potential of utilizing semantic networks as a means of efficiently storing and retrieving the Generalized Stochastic Colored Petri Nets from which the error recovery plans are derived.

Farah, Jeffrey J.↗