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Artificial intelligence systems integration
The core idea of A.I. systems integration is making individual software components, such as speech synthesizers, interoperable with other components, such as common sense knowledgebases, in order to create larger, broader and more capable A.I. systems. The main methods that have been proposed for integration are message routing, or communication protocols that the software components use to communicate with each other, often through a middleware blackboard system.
Most artificial intelligence systems involve some sort of integrated technologies, for example the integration of speech synthesis technologies with that of speech recognition. However, in recent years there has been an increasing discussion on the importance of systems integration as a field in its own right. Proponents of this approach are researchers such as Marvin Minsky, Aaron Sloman, Deb Roy, Kristinn R. Thórisson and Michael A. Arbib. A reason for the recent attention A.I. integration is attracting is that there have already been created a number of (relatively) simple A.I. systems for specific problem domains (such as computer vision, speech synthesis, etc.), and that integrating what's already available is a more logical approach to broader A.I. than building monolithic systems from scratch.
== Why integration? ==
The focus on systems integration, especially with regard to modular approaches, derive from the fact that most intelligences of signifact scales are composed of a multitude of processes and/or utilize multimodal input and output. For example, a humanoid-type of intelligence would preferably have to be able to talk using speech synthesis, hear using speech recognition, understand using a logical (or some other undefined) mechanism, and so forth. In order to produce artificially intelligent software of broader intelligence, integration of these modalities is necessary.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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