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/vol02/dn_cmplx/ | © Copyright 1995 | |||
| Volume 02 | Received: Accepted: |
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PFSA Modelling of Behavioural Sequences by Evolutionary Programming
Charlie H. Clelland and Douglas A. Newlands |
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| Abstract | |
| Behavioural observations can often be described as a sequence of symbols drawn from a finite alphabet. However, the inductive inference of such strings by any automated technique to produce models of the data is a nontrivial task. This paper considers modelling of behavioural data using probabilistic finite state automata (PFSAs). There are a number of information-theoretic techniques for evaluating possible hypotheses. The measure used in this paper is the minimum message length (MML) of Wallace. Although attempts have been made to construct PFSA models by incremental addition of substrings using heuristic rules and the MML to give the lowest information cost, the resultant models cannot be shown to be globally optimal. Fogel's evolutionary programming can produce globally optimal PSFA models by evolving data structures of arbitrary complexity without the requirement to encode the PFSA into binary strings as in genetic algorithms. However, evaluation of PSFAs during the evolution process by the MML of the PFSA alone is not possible since there will be symbols which cannot be consumed by a partially correct solution. It is suggested that the addition of a "can't consume" symbol-to-symbol alphabet obviates this difficulty. The addition of this null symbol to the alphabet also permits the evolution of explanatory models which need not explain all of the data, a useful property to avoid overfitting noisy data. Results are given for two test sets for which the optimal PFSA model is known and for two sets of eye-glance data derived from an instrument panel simulator. | |
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