Learning Dynamics System Identi cation for Perceptually Challenged Agents

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 identi cation as inference of deterministic nite state automata from sequences of input output pairs The agent can in uence 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 e cient high probability learning algorithms for a number of system identi cation problems involving such errors We also present the results of empirical investigations applying these algorithms to learning spatial representations This work was supported in part by a National Science Foundation Presidential Young Investigator Award IRI by the Air Force and the Advanced Research Projects Agency of the Department of Defense under Contract No F C and by the National Science foundation in conjunction with the Advanced Research Projects Agency of the Department of Defense under Contract No IRI This work was supported in part by a National Science Foundation National Young Investigator Award