Learning regular sets from queries and counterexamples
Information and Computation
Inference of finite automata using homing sequences
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
The minimum consistent DFA problem cannot be approximated within and polynomial
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Diversity-based inference of finite automata
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
Coping with uncertainty in map learning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Learning dynamics: system identification for perceptually challenged agents
Artificial Intelligence
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We assume that it is useful for a robot to construct a spatial representation of its environment for navigation purposes. In addition, we assume that robots, like people, make occasional errors in perceiving the spatial features of their environment. Typical perceptual errors include confusing two distinct locations or failing to identify the same location seen at different times. We are interested in the consequences of perceptual uncertainty in terms of the time and space required to learn a map with a given accuracy. We measure accuracy in terms of the probability that the robot correctly identifies a particular underlying spatial configuration. We derive considerable power by providing the robot with routines that allow it to identify landmarks on the basis of local features. We provide a mathematical model of the problem and algorithms that are guaranteed to learn the underlying spatial conliguration for a given class of environments with probability 1 - δ in time polynomial in 1/δ and some measure of the structural complexity of the environment and the robot's ability to discern that structure. Our algorithms apply to a variety of environments that can be modeled as labeled graphs or deterministic finite automata.