An Overview of Computational Intelligence Methods

Abstract

Computational Intelligence is a relatively new branch of science which, to some extent, can be regarded as a successor of “traditional” Artificial Intelligence. Unlike AI, which relies on symbolic learning and heuristic approaches to problem solving, CI mainly involves systems that are inspired from nature, such as (artificial) neural networks, evolutionary computation, fuzzy systems, chaos theory, probabilistic methods, swarm intelligence, ant systems, and artificial immune systems. In a wider perspective CI includes also part of machine learning, in particular the reinforcement learning methods.

Applying either one or a combination of the above-mentioned disciplines allows implementation of the elements of learning and adaptation in the proposed solutions which make such systems somehow intelligent.

In the game playing domain the most popular CI disciplines are neural networks, evolutionary and neuro-evolutionary methods, and reinforcement learning. These domains are briefly introduced in the remainder of this chapter along with sample game-related applications. The focus of the presentation is on the aspects of learning and autonomous development. Relevant literature is provided for possible further reading.