Title

Learning situational knowledge through observation of expert performance in a simulation-based environment

Abstract

This paper outlines an efficient method to gather, represent, and learn expert knowledge by examining the expert's simulated surroundings while simultaneously monitoring the expert's actions for a given situation. It uses recent advances in the areas of neural networks and artificial intelligence to establish a suitable knowledge and representation schema that incorporates both numeric and symbolic forms of knowledge. The method demonstrates the ability to train on basic skills and to generalize learned actions to handle more complex situations not previously encountered.

Publication Date

1-1-1994

Publication Title

Southcon Conference Record

Number of Pages

65-70

Document Type

Article; Proceedings Paper

Identifier

scopus

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/southc.1994.498077

Socpus ID

0028747513 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0028747513

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