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The objectives of Distributed Artificial Intelligence are to solve the reasoning, planning, learning and perception problems of artificial intelligence, especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requires:
The second wave blossomed in the late 1980s, following a 1987 book about Parallel Distributed Processing by James L. McClelland, David E. Rumelhart et al., which introduced a couple of improvements to the simple perceptron idea, such as intermediate processors (now known as "hidden layers") alongside input and output units, and used a sigmoid ...
Human-based computation (apart from the historical meaning of "computer") research has its origins in the early work on interactive evolutionary computation (EC). [9] The idea behind interactive evolutionary algorithms has been attributed to Richard Dawkins; in the Biomorphs software accompanying his book The Blind Watchmaker (Dawkins, 1986) [10] the preference of a human experimenter is used ...
Distributed cognition is an approach to cognitive science research that was developed by cognitive anthropologist Edwin Hutchins during the 1990s. [1]From cognitive ethnography, Hutchins argues that mental representations, which classical cognitive science held that are within the individual brain, are actually distributed in sociocultural systems that constitute the tools to think and ...
Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers. [1] [2] The components of a distributed system communicate and coordinate their actions by passing messages to
Stream processing is especially suitable for applications that exhibit three application characteristics: [citation needed] Compute intensity, the number of arithmetic operations per I/O or global memory reference. In many signal processing applications today it is well over 50:1 and increasing with algorithmic complexity.
Distributed language is a concept in linguistics that language is not an independent symbolic system used by individuals for communication but rather an array of behaviors that constitute human interaction. [1] The concept of distributed language is based on a biological theory of the origin of language and the concept of distributed cognition.
Distributed data processing. Distributed data processing [1] (DDP) [2] was the term that IBM used for the IBM 3790 (1975) and its successor, the IBM 8100 (1979). Datamation described the 3790 in March 1979 as "less than successful." [3] [4] Distributed data processing was used by IBM to refer to two environments: IMS DB/DC; CICS/DL/I [5] [6]