Leslie Kaelbling

Publications (2)1.59 Total impact

• Chapter: Uncertainty in Graph-Based Map Learning

  Thomas Dean, Kenneth Basye, Leslie Kaelbling
  [show abstract] [hide abstract]
  ABSTRACT: For certain applications it is useful for a robot to predict the consequences of its actions. As a particular example, consider programming a robot to learn the spatial layout of its environment for navigation purposes. For this problem it is useful to represent the interaction of the robot with its environment as a deterministic finite automaton. In map learning the states correspond tolocally distinctive placesthe inputs to robot actions (navigation procedures), and the outputs to the information available through observation at a given place. In general, it is not possible to infer the exact structure of the underlying automaton(e.g.the robot’s sensors may not allow it to discriminate among distinct structures). However, even learning just thediscernible structure of its environment is not an easy problem when various types of uncertainty are considered. In this chapter we will examine the effects of only having probablistic information about transitions between states and only probablistic knowledge of the identity of the current state. Using this theoretical framework we can then determine whether it is at all possible for a given robot to learn some specific environment and, if so, how long this can be expected to take.
  01/2011: pages 171-192;
• Source

  Available from: cs.brown.edu

  Article: Inferring finite automata with stochastic output functions and an application to map learning

  Thomas Dean, Dana Angluin, Kenneth Basye, Sean Engelson, Leslie Kaelbling, Evangelos Kokkevis, Oded Maron
  [show abstract] [hide abstract]
  ABSTRACT: It is often useful for a robot to construct a spatial representation of its environment from experiments and observations, in other words, to learn a map of its environment by exploration. In addition, robots, like people, make occasional errors in perceiving the spatial features of their environments. We formulate map learning as the problem of inferring from noisy observations the structure of a reduced deterministic finite automaton. We assume that the automaton to be learned has a distinguishing sequence. Observation noise is modeled by treating the observed output at each state as a random variable, where each visit to the state is an independent trial and the correct output is observed with probability exceeding 1/2. We assume no errors in the state transition function.Using this framework, we provide an exploration algorithm to learn the correct structure of such an automaton with probability 1–, given as inputs , an upper boundm on the number of states, a distinguishing sequences, and a lower bound >1/2 on the probability of observing the correct output at any state. The running time and the number of basic actions executed by the learning algorithm are bounded by a polynomial in –1,m, |s|, and (1/2–)–1.We discuss the assumption that a distinguishing sequence is given, and present a method of using a weaker assumption. We also present and discuss simulation results for the algorithm learning several automata derived from office environments.
  Machine Learning 12/1994; 18(1):81-108. · 1.59 Impact Factor