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The complex interplay of heterogeneous reasoning modes that is involved in this process is orchestrated by a special first-order logic system that uses static abstraction levels, dynamic declarative meta control, and a simple form of truth maintenance in order to test models quickly and cheaply.
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Policy Enforcing Agents rely on meta-control strategies to dynamically interleave semantic web reasoning and service discovery and access.
Meta-control rules can be customized to best capture the requirements associated with different domains and different sets of policies.
We demonstrate that Hebbian learning of synaptic weights alone is not adequate for all tasks, and that additional meta-control processes should be involved.
This article introduces a semantic web framework and a meta-control model to orchestrate policy reasoning with the identification and access of relevant sources of information.
In contrast with the rather Stereotypie behavior of typical AI agents, an AIS must adapt several key aspects of its behavior to its dynamic situation: its perceptual strategy, its control mode, its choices of reasoning tasks to perform, its choices of reasoning methods for performing chosen tasks; and its meta-control strategy for global coordination of all its behavior.
The result is an efficient non-myopic solution to the meta-level control problem for anytime algorithms.
This paper analyzes the characteristics of existing techniques for meta-level control of anytime algorithms and develops a new framework for monitoring and control.
Meta-criteria Access control Meta-criteria Accessacontrolof security objects.
Quite interestingly, the NIS1 locus is syntenic to a meta QTL controlling NUE in several Monocots (Quraishi et al. 2011).
A fusion of intelligent computing is applied for development of ATFM using advantages of the meta-level control approach in (Alves et al. 2008).
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