In a computational context, self-awareness (SA) is a capability of an autonomous system to describe the acquired experience about itself and its surrounding environment with appropriate models and correlate them incrementally with the currently perceived situation to expand its knowledge continuously. This article introduces a bio-inspired framework for generative and descriptive dynamic models that support SA computationally and efficiently. Generative models facilitate predicting future states, while descriptive models enable the selection of the representation that best fits the current observation. Our framework is founded on the analysis and extension of three bio-inspired theories that have studied SA from different viewpoints, and we demonstrate how probabilistic techniques, such as cognitive dynamic Bayesian networks and generalized filtering paradigms, can learn appropriate models from multidimensional proprioceptive and exteroceptive signals acquired by the autonomous system. We discuss essential capabilities for SA and show how our modeling framework supports these capabilities in theory and through a case study where a mobile robot uses multisensorial data to determine its internal and environmental state as well as distinguishing among normal and abnormal behaviors.

中文翻译：

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自主系统的多感官生成和描述性自我意识模型


在计算环境中，自我意识（SA）是自治系统通过适当的模型描述所获得的有关自身及其周围环境的经验的能力，并将它们与当前感知的情况逐步关联起来，以不断扩展其知识。本文介绍了一种用于生成和描述性动态模型的仿生框架，该模型可高效地支持 SA 计算。生成模型有助于预测未来状态，而描述性模型可以选择最适合当前观察的表示。我们的框架建立在对三种仿生理论的分析和扩展之上，这些理论从不同的角度研究了 SA，并且我们演示了概率技术（例如认知动态贝叶斯网络和广义过滤范式）如何从多维本体感受和外感受学习适当的模型自主系统获取的信号。我们讨论了 SA 的基本功能，并通过案例研究展示了我们的建模框架如何在理论上支持这些功能，其中移动机器人使用多传感器数据来确定其内部和环境状态以及区分正常和异常行为。