Ethology-Based Approximate Adaptive Learning: A Near Set Approach

Abstract

Introduction

The problem considered in this paper is how learning by a machine can adapt its behaviour to changing environmental conditions to achieve a better result. The solution to this problem hearkens back to the work of ethologist Niko Tinbergen (1940, 1942, 1948, 1951, 1953, 1963), starting in the 1940s. Tinbergen (1953b) suggested that the behaviour of swarms of interacting organisms and their environment make swarms be seen as individual. Of course, the insight in Tinbergen’s work augurs later by those who were interested in adaptive learning by societies of interacting machines. The work by Tinbergen and Konrad Lorenz (1981) led to the introduction of ethology, a comparative science of behaviour. The basic idea in the proposed approach to adaptive learning is to look behaviour of an organism as episodic and to record observed behaviours ethograms. An ethogram is a tabular representation of observed behaviours. An ethogram is a tabular representation of observed behaviours during an episode. Let s_i, a_i, r_i denote the i^th state, action, reward, respectively. Reward r_i results from performing action a_i, where 0 ≤ i ≤ n for some finite, positive integer n. Each episode consists of a finite state-action-reward sequence of the form . In this chapter, adaptive learning itself is observed at the individual level as well as at the society level.