Learning About Systems that Contain State Variables

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 4

Learning

It is difficult to learn about systems that contain state variables when those variables are not directly observable. This paper formalizes this learning problem and presents a method called the iterative extension method for solving it. In the iterative extension method, the learner gradually constructs a partial theory of the state-containing system. At each stage, the learner applies this partial theory to interpret the I/O behavior of the system and obtain additional constraints on the structure and values of its state variables. These constraints can be applied to extend the partial theory by hypothesizing additional internal state variables. The improved theory can then be applied to interpret more complex I/O behavior. This process continues until a theory of the entire system is obtained. Several sufficient conditions for the success of this method are presented including (a) the observability and decomposability of the state information in the system. (b) the learnability of individual state transitions in the system, (c) the ability of the learner to perform synthesis of straight-line programs and conjunctive predicates from examples and (d) the ability of the learner to perform theory-driven data interpretation. The method is being implemented and applied to the problem of learning UNIX file system commands by observing a tutorial interaction with UNIX.