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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 International Parallel and Distributed Processing Symposium (or IPDPS) is an annual conference for engineers and scientists to present recent findings in the fields of parallel processing and distributed computing. In addition to technical sessions of submitted paper presentations, the meeting offers workshops, tutorials, and commercial ...
CADP [1] (Construction and Analysis of Distributed Processes) is a toolbox for the design of communication protocols and distributed systems. CADP is developed by the CONVECS team (formerly by the VASY team) at INRIA Rhone-Alpes and connected to various complementary tools.
Information Processing Letters is a peer-reviewed scientific journal in the field of computer science, published by Elsevier. The aim of the journal is to enable fast dissemination of results in the field of information processing in the form of short papers. Submissions are limited to nine double-spaced pages.
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 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
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]
Problems and challenges related to distributed computing, distributed systems and distributed algorithms, including: Computational problems that have been studied in a distributed setting. Challenges related to dividing a computational problem into multiple tasks that can be solved in parallel. Challenges related to fault-tolerance and ...