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offsprings for survival. Connection learning recognizes different input patterns
through training the neural networks with typical example instances.
Machine learning research is still in its primary stage, and needs extensive
research efforts. Progress in machine learning research will enable breakthroughs
in AI and knowledge engineering research. In the future, research focuses of
machine learning will include cognitive models for the learning process,
computational learning theories, new learning algorithms, machine learning
systems integrating multiple learning strategies, etc.
1.8 Distributed Artificial Intelligence
Studies of human intellectual behaviors show that most human activities involve
social groups consisting of multiple individuals, and large-scale complex
problem solving also involves cooperation of several professionals or
organizations. “Cooperation” is a major aspect of human intelligence pervasive
in the human society, and thus the motivation for research in Distributed
Artificial Intelligence (DAI).
With the development of computer network, computer communication and
concurrent programming technologies since the 1980's, DAI is gradually
becoming a new research focus in the field of AI. DAI is a subfield of AI
investigating how logically and physically distributed agents cooperate with each
other to perform intelligent behaviors. It enables collaborated and coordinated
knowledge, skills and planning, solves single-objective and multi-objective
problems, and provides an effective means for the design and construction of
large-scale complex intelligent systems or computers to support cooperation.
The term DAI was coined by American researchers, and the first International
Workshop on Distributed Artificial Intelligence was held at MIT in Boston,
U.S.A. in 1980. From then on, all kinds of conferences on DAI or DAI related
topics have been held continually all over the world, which greatly promotes the
development and popularization of DAI technologies, and gradually deepens and
broadens the research and applications of the science of DAI. With the increase
in scale, scope and complexity of new computer based information systems,
decision support systems and knowledge based systems, as well as the
requirement to encode more complex knowledge in these systems, applications
and development of DAI technologies is becoming increasingly important to
these systems.
