Discovering the Structure of a Reactive Environment by Exploration

Author:

Mozer Michael C.1,Bachrach Jonathan2

Affiliation:

1. Department of Computer Science and Institute of Cognitive Science, University of Colorado, Boulder, CO 80309-0430 USA

2. Department of Computer and Information Science, University of Massachusetts, Amherst, MA 01003 USA

Abstract

Consider a robot wandering around an unfamiliar environment, performing actions and observing the consequences. The robot's task is to construct an internal model of its environment, a model that will allow it to predict the effects of its actions and to determine what sequences of actions to take to reach particular goal states. Rivest and Schapire (1987a,b; Schapire 1988) have studied this problem and have designed a symbolic algorithm to strategically explore and infer the structure of “finite state” environments. The heart of this algorithm is a clever representation of the environment called an update graph. We have developed a connectionist implementation of the update graph using a highly specialized network architecture. With backpropagation learning and a trivial exploration strategy — choosing random actions — the connectionist network can outperform the Rivest and Schapire algorithm on simple problems. Our approach has additional virtues, including the fact that the network can accommodate stochastic environments and that it suggests generalizations of the update graph representation that do not arise from a traditional, symbolic perspective.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction;2021 60th IEEE Conference on Decision and Control (CDC);2021-12-14

2. Group-Linking Method: A Unified Benchmark for Machine Learning with Recurrent Neural Network;IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences;2007-12-01

3. A neural-network architecture for syntax analysis;IEEE Transactions on Neural Networks;1999

4. Learning metric-topological maps for indoor mobile robot navigation;Artificial Intelligence;1998-02

5. Dynamic On-line Clustering and State Extraction: An Approach to Symbolic Learning;Neural Networks;1998-01

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