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HETEROGENEOUS COMPUTING AND MODELS

HETEROGENEOUS COMPUTING

In Nourani (1998) we present new techniques for design by software agents and new concepts entitled Abstract Intelligent Implementation (AII) of AI systems. Objects, message passing actions, and implementing agents are defined by syntactic constructs, with agents appearing as functions. The techniques have been applied to design intelligent business objects in Nourani (1998). AII techniques have been applied to Heterogeneous KB Design and implementation. The application areas include support for highly responsive planning. AII techniques are due to be an area of crucial importance as they are applied gradually to the real problems. The applied fields are, for example, intelligent business systems, aerospace, AI for robots, and multimedia.

KNOWLEDGE BASES AND VISUAL MODEL DISCOVERY

Model diagrams allow us to characterize incomplete KR. To key into the incomplete knowledge base we apply generalized predictive diagrams, whereby specified diagram functions on a search engine can select onto localized data fields. The predictive model diagrams (Nourani, 1995a, 1999a) could be minimally represented by the set of functions {f1, ..., fn} that inductively define the model. Data discovery from KR on diagrams might be viewed as satisfying a goal by getting at relevant data which instantiates a goal. The goal formula states what relevant data is sought. We propose methods that can be applied to planning (Nourani, 1991) with diagrams to implement discovery planning. In planning with G-diagrams that part of the plan that involves free Skolemized trees is carried along with the proof tree for a plan goal. Computing with diagram functions allows us to key to active visual databases with agents.

 
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