Production System Rules as Protein Complexes from Genetic Regulatory Networks
This is an incremental improvement for rule-based systems and genetic regulatory networks in AI.
The paper tackles the problem of designing production system rules by introducing an indirect encoding scheme that views rules as protein complexes generated by artificial genetic regulatory networks. The result shows competitive performance with related methods on benchmark problems.
This short paper introduces a new way by which to design production system rules. An indirect encoding scheme is presented which views such rules as protein complexes produced by the temporal behaviour of an artificial genetic regulatory network. This initial study begins by using a simple Boolean regulatory network to produce traditional ternary-encoded rules before moving to a fuzzy variant to produce real-valued rules. Competitive performance is shown with related genetic regulatory networks and rule-based systems on benchmark problems.