Abstract
This paper presents an architecture for acquiring spatio-temporal knowledge. This architecture uses two different algorithms — generalization to interval (GTI) method and feature construction method — for learning from sensory/perceptual information. These methods generalize over positive/negative examples of target knowledge, and output a constraint program that can be used declaratively as a learned concept about spatio-temporal patterns, and procedurally as a method for reasoning about spatio-temporal relations. Thus our methods transform numeric spatio-temporal patterns to symbolic declarative/procedural representations. We have implemented these two algorithms with acorn, a system that acquires spatio-temporal knowledge by observing examples. In this paper, we give two examples from different domains -layout problems and robot-commands learning — to demonstrate the ability of the system and the flexibility of constraint programs for knowledge representation.
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© 1992 Springer-Verlag Berlin Heidelberg
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Hiraki, K., Anzai, Y. (1992). Towards acquiring spatio-temporal knowledge from sensor data. In: Frank, A.U., Campari, I., Formentini, U. (eds) Theories and Methods of Spatio-Temporal Reasoning in Geographic Space. Lecture Notes in Computer Science, vol 639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55966-3_22
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DOI: https://doi.org/10.1007/3-540-55966-3_22
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