A system, apparatus and application for providing robots with the ability to intelligently respond to perceived situations are described. A knowledge database is assembled automatically, based on distributed knowledge capture. The knowledge base embodies the 'common sense,' that is, the consensus, of the subjects who contribute the knowledge. Systems are provided to automatically preprocess, or 'clean' the information to make it more useful. The knowledge thus refined is utilized to construct a multidimensional semantic network, or MSN. The MSN provides a compact and efficient semantic representation suitable for extraction of knowledge for inference purposes and serves as the basis for task and response selection. When the robot perceives a situation that warrants a response, an appropriate subset of the MSN is extracted into a Bayes network. The resultant network is refined, and used to derive a set of response probabilities, which the robot uses to formulate a response.
RESPONDING TO SITUATIONS USING KNOWLEDGE REPRESENTATION AND INFERENCE