29 March 1988 Creating Input Sets For Inductive Learning From Simple Events
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Autonomous machines such as a planetary explorer must learn from the real universe, which can be seen as a single event of infinite complexity or as an infinite set of trivial events. Current inductive learning systems generalize from a finite set of events provided by a human teacher or a software environment. Even a constrained universe is difficult to represent as a finite set of events comprising various types of useful information. Cues used by humans to perform this representation task include temporal or spatial relationships among environmental data. We discuss a method for identifying meaningful complex events in the universe of an autonomous learning robot. When no temporal concept is known to link two descriptors, temporal proximity of events is used to pair simple descriptor events into complex events for inductive learning. When a temporal concept is known to link two descriptors, that concept is used to guide the pairing of descriptor events. We discuss the efficiency improvements which arise from using temporal concepts in this manner. The method is embedded in GPAL 1.3, a general purpose autonomous learner that uses a knowledge-based learning and control strategy which models the scientific method.
© (1988) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brian Tillotson, Charlotte Lin, and James Bezdek "Creating Input Sets For Inductive Learning From Simple Events", Proc. SPIE 0937, Applications of Artificial Intelligence VI, (29 March 1988); doi: 10.1117/12.946984; https://doi.org/10.1117/12.946984

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