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What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with “computational intelligence” in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed.

Artificial Intelligence (AI) has recently become a real formal science: the new millennium brought the first mathematically sound, asymptotically optimal, universal problem solvers, providing a new, rigorous foundation for the previously largely heuristic field of General AI and embedded agents. At the same time there has been rapid progress in practical methods for learning true sequence-processing programs, as opposed to traditional methods limited to stationary pattern association. Here we will briefly review some of the new results, and speculate about future developments, pointing out that the time intervals between the most notable events in over 40,000 years or 2^9 lifetimes of human history have sped up exponentially, apparently converging to zero within the next few decades. Or is this impression just a by-product of the way humans allocate memory space to past events?

The work in the area of computational cognitive modeling explores the essence of cognition through developing detailed understanding of cognition by specifying computational models. In this enterprise, a cognitive architecture is a domain-generic computational cognitive model that may be used for a broad, multiple-domain analysis of cognition. It embodies generic descriptions of cognition in computer algorithms and programs. Building cognitive architectures is a difficult task and a serious challenge to the fields of cognitive science, artificial intelligence, and computational intelligence. In this article, discussions of issues and challenges in developing cognitive architectures will be undertaken, examples of cognitive architectures will be given, and future directions will be outlined.

There is a complex relationship between the architecture of a computer, the software it needs to run, and the tasks it performs. The most difficult aspect of building a brain-like computer may not be in its construction, but in its use: How can it be programmed? What can it do well? What does it do poorly? In the history of computers, software development has proved far more difficult and far slower than straightforward hardware development. There is no reason to expect a brain like computer to be any different. This chapter speculates about its basic design, provides examples of “programming” and suggests how intermediate level structures could arise in a sparsely connected massively parallel, brain like computer using sparse data representations.

An approach to intelligence and reasoning is developed for the brain. The need for such an approach to Computational Intelligence is argued for on ethical grounds. The paper then considers various components of information processing in the brain, choosing attention, memory and reward as key (Language cannot be handled in the space available). How these could then be used to achieve cognitive faculties and ultimately reasoning are then discussed, and the paper concludes with a brief analysis of reasoning tasks, including the amazing powers of Betty the Crow.

The Korean Brain Neuroinformatics Research Program has dual goals, i.e., to understand the information processing mechanism in the brain and to develop intelligent machine based on the mechanism. The basic form of the intelligent machine is called Artificial Brain, which is capable of conducting essential human functions such as vision, auditory, inference, and emergent behavior. By the proactive learning from human and environments the Artificial Brain may develop oneself to become more sophisticated entity. The OfficeMate will be the first demonstration of these intelligent entities, and will help human workers at offices for scheduling, telephone reception, document preparation, etc. The research scopes for the Artificial Brain and OfficeMate are presented with some recent results.

The brain has been a source of inspiration for artificial intelligence since long. With the advance of modern neuro-imaging techniques we have the opportunity to peek into the active brain in normal human subjects and to measure its activity. At the present, there is a large gap in knowledge linking results about neuronal architecture, activity of single neurons, neuro-imaging studies and human cognitive performance. Bridging this gap is necessary before we can understand the neuronal encoding of human cognition and consciousness and opens the possibility for Brain- Computer Interfaces (BCI). BCI applications aim to interpret neuronal activity in terms of action or intention for action and to use these signals to control external devices, for example to restore motor function after paralysis in stroke patients. Before we will be able to use neuronal activity for BCI-applications in an efficient and reliable way, advanced pattern recognition algorithms have to be developed to classify the noisy signals from the brain. The main challenge for the future will be to understand neuronal information processing to such an extent that we can interpret neuronal activity reliably in terms of cognitive activity of human subjects. This will provide insight in the cognitive abilities of humans and will help to bridge the gap between natural and artificial intelligence.

A remarkable achievement of the perceptual system is its scene analysis capability, which involves two basic perceptual processes: the segmentation of a scene into a set of coherent patterns (objects) and the recognition of memorized ones. Although the perceptual system performs scene analysis with apparent ease, computational scene analysis remains a tremendous challenge as foreseen by Frank Rosenblatt. This chapter discusses scene analysis in the field of computational intelligence, particularly visual and auditory scene analysis. The chapter first addresses the question of the goal of computational scene analysis. A main reason why scene analysis is difficult in computational intelligence is the binding problem, which refers to how a collection of features comprising an object in a scene is represented in a neural network. In this context, temporal correlation theory is introduced as a biologically plausible representation for addressing the binding problem. The LEGION network lays a computational foundation for oscillatory correlation, which is a special form of temporal correlation. Recent results on visual and auditory scene analysis are described in the oscillatory correlation framework, with emphasis on real-world scenes. Also discussed are the issues of attention, feature-based versus model-based analysis, and representation versus learning. Finally, the chapter points out that the time dimension and David Marr's framework for understanding perception are essential for computational scene analysis.

This chapter discusses opportunities and challenges for the creation of methods of computational intelligence (CI) and more specifically – artificial neural networks (ANN), inspired by principles at different levels of information processing in the brain: cognitive-, neuronal-, genetic-, and quantum, and mainly, the issues related to the integration of these principles into more powerful and accurate CI methods. It is demonstrated how some of these methods can be applied to model biological processes and to improve our understanding in the subject area, along with other – being generic CI methods applicable to challenging generic AI problems. The chapter first offers a brief presentation of some principles of information processing at different levels of the brain, and then presents brain-inspired, geneinspired and quantum inspired CI. The main contribution of the chapter though is the introduction of methods inspired by the integration of principles from several levels of information processing, namely: (1) a computational neurogenetic model, that combines in one model gene information related to spiking neuronal activities; (2) a general framework of a quantum spiking neural network model; (3) a general framework of a quantum computational neuro-genetic model. Many open questions and challenges are discussed, along with directions for further research.

Automatic pattern recognition is usually considered as an engineering area which focusses on the development and evaluation of systems that imitate or assist humans in their ability of recognizing patterns. It may, however, also be considered as a science that studies the faculty of human beings (and possibly other biological systems) to discover, distinguish, characterize patterns in their environment and accordingly identify new observations. The engineering approach to pattern recognition is in this view an attempt to build systems that simulate this phenomenon. By doing that, scientific understanding is gained of what is needed in order to recognize patterns, in general.