This paper describes a new modification of fuzzy cognitive maps (FCMs) for the modeling of autonomous entities that make decisions in a dynamic environment. The paper offers a general design for an FCM adjusted for the decision-making of autonomous agents through the categorization of its concepts into three different classes according to their purpose in the map: Needs , Activities , and States (FCM-NAS). The classification enables features supporting decision-making, such as the easy processing of input from sensors, faster system reactions, the modeling of inner needs, the adjustable frequency of computations in a simulation, and self-evaluation of the FCM-NAS that supports unsupervised evolutionary learning. This paper presents two use cases of the proposed extension to demonstrate its abilities. It was implemented into an agent-based artificial life model, where it took advantage of all the above features in the competition for resources, natural selection, and evolution. Then, it was used as decision-making for human activity simulation in an ambient intelligence model, where it is combined with scenario-oriented mechanism proving its modularity.

中文翻译：

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动态环境中决策的模糊认知图

本文描述了模糊认知图 (FCM) 的新修改，用于对在动态环境中做出决策的自主实体进行建模。本文提供了 FCM 的一般设计，通过根据它们在地图中的目的将其概念分为三个不同的类：需求、活动和状态 (FCM-NAS)，为自主代理的决策进行了调整。分类支持决策制定的功能，例如轻松处理来自传感器的输入、更快的系统反应、内部需求的建模、模拟中计算的可调频率以及支持无监督的 FCM-NAS 的自我评估进化学习。本文介绍了所提议扩展的两个用例，以展示其能力。它被实现为基于代理的人工生命模型，在资源竞争、自然选择和进化中利用了上述所有特征。然后，它被用作环境智能模型中人类活动模拟的决策，并与面向场景的机制相结合，证明了其模块化。