Preface to Part II Computational Intelligence and Machine Learning

In computation intelligence and machine learning researchers often look to other fields of study, such as anthropology, biology, computer science, psychology, philosophy and neuroscience to shape the algorithms being developed. Intelligence, which is generally attributed to humans, animals and plants, can be characterized by abstract thought, understanding, communication, reasoning, learning, planning, emotional intelligence and problem solving. At the center of this capability are intelligent systems that have both the ability to adapt and the necessary complex analytics for autonomous behaviour that can provide intelligent control. Such abilities can aid those in industry and academia currently searching for novel and adaptable solutions to complex problems. Within industry, the main priorities for business and government research and development include decentralization, uncertainly and complexity. In the coming years there will be a need for programmatic efficiencies because of the insufficient manpower to support complex missions. There will also be harsh environments that do not reasonably permit humans to enter and sustain activity as well as new requirements for adaptive autonomous control of vehicle systems in the face of unpredictable environments and challenging missions. Problems of uncertainty and brittleness in this ever changing environment will need to be resolved with machine reasoning and intelligence, human/autonomous system interaction, and collaboration and scalable teaming of autonomous systems. Neural networks, fuzzy logic and evolutionary programming are just a few of the techniques that have been developed to solve some of these problems. The algorithms, techniques and applications described in this section move the fields of computational intelligence and machine learning in new directions and aim to contribute to the Complex Adaptive Systems Conference (2012) in the following categories: