CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Self-Organizing Maps
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This paper presents our first efforts toward learning simple logical representations from robot sensory data and thus toward a solution for the perceptual grounding problem [2]. The elements of representations learned by our method are states that correspond to stages during the robot's experiences, and atomic propositions that describe the states. The states are found by an incremental hidden Markov model induction algorithm; the atomic propositions are immediate generalizations of the probability distributions that characterize the states. The state induction algorithm is guided by the minimum description length criterion: the time series of the robot's sensor values for several experiences are redescribed in terms of states and atomic propositions and the model that yields the shortest description (of both model and time series) is selected.