使用SODA方法集成认知架构和计算智能

Angela Consoli
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引用次数: 1

摘要

智能多主体系统(I-MASs)的通用性和应用正变得越来越复杂。复杂性的增加是由于突然需要智能代理(IAs)和I-MASs来展示更多类似人类的功能。这包括用感知、学习、识别、推理和理性来表达知识的能力。此外,这个功能的“愿望清单”也需要限制在当前技术的范围内。I-MASs的设计者正在寻找能够满足更多复杂需求的机制和流程。两个这样的范例是认知架构和计算智能。Agent Coordination and Cooperation Cognitive Model (ac3m)主要阐述了Agent Coordination and Cooperation之间的联系。在分析ac3m时,发现了刺激-定向-决定-行动(SODA)方法。这种方法是将认知架构原理与计算智能相结合的结果。SODA是信念、欲望和意图(BDI)方法与观察-定向-决定-行动(OODA)循环的整合,结果是BDI的组成部分与OODA循环的各个阶段相关联。SODA方法的主要优点是增强了代理的态势感知,这在团队自动化领域已被证明是至关重要的。
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The integration of cognitive architectures and computational intelligence using the SODA methodology
The versatility and application of Intelligent Multi-Agent Systems (I-MASs) is becoming more complex. The increase in complexity is due to a sudden requirement for Intelligent Agents (IAs) and I-MASs to exhibit more human-like functionality. Thisincludes the abilityto represent knowledge using perception, learning, recognition, reasoning and rationality. In addition, this "wishlist" of functionality also needs to be constrained within the limits of current technology. Designers of I-MASs are looking at mechanisms and processes that can satisfy more involved requirements. Two such paradigms are cognitive architectures and computational intelligence. The Agent Coordination and Cooperation Cognitive Model (AC 3 M) was developed to primarily illustrate the link between agent coordination and cooperation. Whilst analysing AC 3 M, the Stimulate-Orient-Decide-Act (SODA) methodology was discovered. This methodology is a result of integrating cognitive architecture principles with computational intelligence. SODA was the integration of the Belief, Desire and Intention (BDI) methodology with the Observe-Orient-Decide-Act (OODA) loop and results in the components of BDI being linked to phases of the OODA loop. The main advantage of the SODA methodology is the enhancement of an agent's situational awareness, which has proven to be paramount in the area of team automation.
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