Biologically-Inspired Adaptive Learning: A Near Set Approach

The problem considered in this paper is how learning by machines can be influenced beneficially by various forms of learning by biological organisms. The solution to this problem is partially solved by considering considering a model of perception that is at the level of classes in a partition defined by a particular equivalence relation in an approximation space. This form of perception provides a basis for adaptive learning that has surprising acuity. Viewing approximation spaces as the formal counterpart of perception was suggested by Ewa Ortowska in 1982. This view of perception grew out the discovery of rough sets by Zdzistaw Pawlak during the early 1980s. The particular model of perception that underlies biologically-inspired learning is based on a near set approach, which considers classes of organisms with similar behaviours. In this paper, the focus is on learning by tropical fish called glowlight tetra (Hemigarmmus erythrozonus). Ethology (study of the comparative behaviour of organisms), in particular, provides a basis for the design of an artificial ecosystem useful in simulating the behaviour of fish. The contribution of this paper is a complete framework for an ethology-based study of adaptive learning defined in the context of nearness approximation spaces.