In 1992 Japan's Ministry of International Trade and Industry (MITI) began a research program in Real World Computing, as a successor to the Fifth Generation Computing program of the previous decade, complementing the fifth-generation approach. Its objective is to lay a foundation and to pursue the technical realization of humanlike flexible and intelligent information processing. This book collects results of ten years of original research by six research laboratories, three Japanese and three European, whose research focus has been the theoretical and algorithmic foundations of intelligence as manifested in the real world an in our dealing with it.
Real-world intelligent systems handle complex, uncertain, dynamic, multimodal information in real time. Both explicit and implicit information are important. Hence we need to develop a novel integrated framework of representing knowledge and making inferences based in it. It is impossible to preprogram all the knowledge needed for coping with the variety and complexity of real environments, and therefore learning and adaptation are keys to intelligence. Learning is a kind of metaprogramming strategy. Instead of writing programs for specific tasks, we must write programs that modify themselves based on a system's interaction with its environment.
The book includes chapters on Inference and learning with graphical models, Approximate reasoning, Evolutionary computation and beyond, Methodology of distributed and active learning, Symbol pattern integration using multilinear functions, and Computing with large random patterns. The treatment is mathematically rigorous, yet accessible, and the discussion of issues is of general interest to an educated reader at large. The book provides excellent reading for graduate courses in Computer Science, Cognitive Science, Artificial Intelligence, and Applied Statistics.