Discrimination learning guided by instinct

Article type: Research Article

Authors: Portegys, Thomas E.

Affiliations: Dialectek, Duvall, WA, USA. E-mail: portegys@gmail.com

Abstract: Complex organisms exhibit both evolved instincts and experiential learning as adaptive mechanisms. In isolation, neither mechanism is sufficient to successfully navigate the environments of such organisms. Instincts provide behaviors that are generally adaptive but fail in specific cases. Learning must rely on some internal or external guidance to succeed on challenging tasks. This paper explores how instincts and experiential learning can work in tandem to solve a maze environment. Specifically, instincts comprise general knowledge of a set of related mazes representing worlds that an organism might be born into, and experiential learning discriminates specific situations in the particular maze world that an organism is born into. Synergy is accomplished by a hybrid neural network, one part instinctive and the other part capable of learning. After sufficient discriminating experiences, learning can override instinct to navigate a maze when instinct would otherwise fail. Results show a marked improvement in performance when this synergistic approach is employed relative to using either instincts or learning in isolation.

Keywords: Hybrid system, neural network, discrimination learning, instinct, maze learning

DOI: 10.3233/HIS-130171

Journal: International Journal of Hybrid Intelligent Systems, vol. 10, no. 3, pp. 129-136, 2013