An architecture and its four load balancing algorithms for a highly OR-parallel inference machine are proposed, and its performance is evaluated in a trace-driven simulation study. This inference machine consists of a...
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An architecture and its four load balancing algorithms for a highly OR-parallel inference machine are proposed, and its performance is evaluated in a trace-driven simulation study. This inference machine consists of a large number of processing elements (PE's) with serial I/O links directly connected to each other in a simply modified mesh network. Each PE is a high-speed sequential Prolog processor with its own local memory. The activity of all PE's is locally controlled by four new load balancing algorithms based on purely local communication. Communication is allowed only between directly connected PE's. These load balancing algorithms reduce communication overhead in a load balancing and make it possible to accomplish highly OR-parallel execution. A software simulator using a trace-driven simulation technique based on an inference tree has been developed, and some typical OR-parallel benchmarks such as the n-queens problem have been simulated on it. The average communication per load balancing is reduced by a factor ranging from 1/30 to 1/100 by the interaction of these load balancing algorithms as compared with a conventional copying method. The inference machine (1024 PE's: 32 x 32 array) attains 300-600 times parallel speedup, assuming 1 MLIPS (mega logical inference per second) PE and a 20 MBPS (mega bit per second) each serial I/O link, which could be easily integrated on a single chip using current VLSI technology. This highly OR-parallel inference machine promises to be an important step towards the realization of a high-performance artificial intelligence system.
Soar is a major exemplar of architectural approach to machine cognition with numerous applications including speech recognition, machine perception, robotic, and strategy planning. We have replaced language used in So...
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ISBN:
(纸本)9781424416738
Soar is a major exemplar of architectural approach to machine cognition with numerous applications including speech recognition, machine perception, robotic, and strategy planning. We have replaced language used in Soar architecture with our own developed XML based Unified Knowledge Manipulation language (UKML) to demonstrate this language as a shared platform for procedural knowledge representation in cognitive architectures, and enhance Soar with some valuable new features including the ability of manipulating XML formatted factual knowledge, greatly improved code readability and organization, and elimination of some ambiguities of Soar traditional language. This paper is a report on this implementation of UMKL, enriched with some examples and their results.
In this paper we address the problem of modeling creativity in Artificial Intelligence using a Genetic or Evolutionary based approach to computing, where the universe of discourse is represented as theories or program...
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ISBN:
(纸本)9781424481262
In this paper we address the problem of modeling creativity in Artificial Intelligence using a Genetic or Evolutionary based approach to computing, where the universe of discourse is represented as theories or programs in an extension to the logic programming language, which makes possible to handle incomplete or even contradictory information in an evolutionary environment. Indeed, we present a new insight for the construction of evolutive systems that combines the potential of the knowledge representation and reasoning mechanisms, present in the logic programming languages. Here, in an evolutionary setting, the candidate solutions to model the universe of discourse are seen as evolutionary logic programs or theories, being the test whether a solution is optimal based on a measure of the quality-of-information carried by those logical theories or programs. From a point of view of the process, the quality-of-information of the universe of discourse is assessed on the fly, being therefore possible to select the best logical theory or program that models it, in terms of the same time line.
Model-driven approaches in developing and operating Cyber-Physical Systems are increasingly complemented by data-driven methods. Examples for their use cases are the analysis of model repositories for discovering patt...
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ISBN:
(纸本)9781728122823
Model-driven approaches in developing and operating Cyber-Physical Systems are increasingly complemented by data-driven methods. Examples for their use cases are the analysis of model repositories for discovering patterns and relationships in models, the design-time learning of approximate system and environment models from data, and the detection of divergence from design-time assumptions during operations. In this paper we argue that model- and data-driven approaches have combined use cases that that need complementary services provided by modeling and data analytic frameworks. However, convergence of model-driven and data-driven methods is hindered by the strongly different tool infrastructure. The paper summarizes the integration challenges and proposes a semantic bridge as a solution for filling the gap between the model - and data-driven tool suites.
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