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
Learning sequential structure in simple recurrent networks
Advances in neural information processing systems 1
Proceedings of the seventh international conference (1990) on Machine learning
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Coping with Uncertainty in Map Learning
Coping with Uncertainty in Map Learning
Diversity-based inference of finite automata
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
Conductance and convergence of Markov chains-a combinatorial treatment of expanders
SFCS '89 Proceedings of the 30th 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
Coping with uncertainty in a control system for navigation and exploration
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Inferring finite automata with stochastic output functions and an application to map learning
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
System identification via state characterization
Automatica (Journal of IFAC)
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence
Topological map induction using neighbourhood information of places
Autonomous Robots
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From the perspective of an agent, the input/output behavior of the environment in which it is embedded can be described as a dynamical system. Inputs correspond to the actions executable by the agent in making transitions between states of the environment. Outputs correspond to the perceptual information available to the agent in particular states of the environment. We view dynamical system identification as inference of deterministic finite-state automata from sequences of input/output pairs. The agent can influence the sequence of input/output pairs it is presented by pursuing a strategy for exploring the environment. We identify two sorts of perceptual errors: errors in perceiving the output of a state and errors in perceiving the inputs actually carried out in making a transition from one state to another. We present efficient, high-probability learning algorithms for a number of system identification problems involving such errors. We also present the results of empirical investigations applying these algorithms to learning spatial representations.