Recently, semiotics has started being the focus of attention of AI researchers due to its interesting capabilities in symbolic processing and knowledge representation. In this paper, we propose the Fielded object Netw...
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ISBN:
(纸本)0780344235
Recently, semiotics has started being the focus of attention of AI researchers due to its interesting capabilities in symbolic processing and knowledge representation. In this paper, we propose the Fielded object Network (FON), a framework aimed at integrating many scientific fields related to artificial intelligence, e.g. artificial life and distributed artificial intelligence (DAI), in order to get a knowledge representation tool capable of performing semiotic processing. Following this trend, this work presents the basis of FON and shows how it can be used to a hierarchical knowledge processing in intelligent systems. To show that, we implement a Generalized Subsistence Machine (GSM) proposed by Meystel [6] ruing a FON approach. We also provide simulation results for an AGV application built under the proposed framework.
Writing portable applications for MIMD (multiple-instruction, multiple-data) architectures has proven to be more difficult than writing sequential software. This is due in large part to the lack of easy-to-use, high-l...
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Writing portable applications for MIMD (multiple-instruction, multiple-data) architectures has proven to be more difficult than writing sequential software. This is due in large part to the lack of easy-to-use, high-level abstractions. Mentat is an object-oriented parallel processing system designed to directly address the difficulty of developing architecture-independent parallel programs. Its fundamental objectives are to provide easy-to-use parallelism, achieve high performance via parallel execution, and facilitate the portability of applications across a wide range of platforms. The premise underlying Mentat is that it is the lack of appropriate abstractions that has kept parallel architectures difficult to program and hence made them inaccessible to mainstream, production system programmers. The Mentat approach combines a medium-grain, data-driven computation model with the object-oriented programming paradigm. The data-driven computation model supports high degrees of parallelism, while the use of the object-oriented paradigm permits hiding much of the parallel environment from the programmer. The Mentat system consists of two components, the Mentat Programming Language (MPL) and the Mentat runtime system. The MPL is an object-oriented programming language based on C++. The programmer is responsible for identifying those classes, called Mentat classes, whose member functions are of sufficient computational complexity to allow efficient parallel execution. Instances of Mentat classes are used like C++ classes. The data and control dependencies between Mentat class instances involved in invocation, communication, and synchronization are detected and managed by the compiler and runtime system without programmer intervention. Mentat is available via anonymous FTP and has been implemented on Sun workstations, the Silicon Graphics Iris, the Intel iPSC/2, and the Intel iPSC/860. This article presents the Mentat programming language, including several examples, the M
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