Adaptive Autonomous Architectural Framework for Intelligent Autonomous Subsystems

人们越来越需要在交互、行为和管理方面更好地理解自治、分布式和智能系统。发现新的方法来解决在协作环境中运行的交互协作自主(ICA)是很有趣的，假设所有的自主都旨在满足不矛盾的总体目标。未来的自治系统预计将在日益复杂和恶劣的环境中运行，响应日益增长的人类需求，并始终以高弹性和适应性保持最佳运行。为了实现这些崇高的目标，应该使用嵌入式工具和算法在开放式体系结构上构建自治，以测量、评估和优化响应环境中的变化。需要测量和评估自治系统之间的相互作用。如果ICA之间的信任交换违反了交换的信任度量所达到的期望，则可能导致自治隔离。考虑到未来大规模智能ICA的动态、敌对和复杂的环境，为了构建、观察、分析和与其交互，重新思考我们如何构建和融合现代自主是很重要的。实现未来ICAS好处的基本概念仍然是促进开放、动态和变化的体系结构，其中体系结构的动态响应其环境的动态。由于智能解决方案倾向于从上下文知识中推断智能，因此构建和分析上下文感知框架对于ICA模型的开发至关重要。虽然ICAS的分类很广，但大多数调查的协作自主侧重于相同或几乎相同的自主之间的相互作用，这一提议超越了这一限制。此外，这一建议在寻求将机器学习应用于交互过程本身的价值方面仍然是独一无二的。这一建议的主要影响是采用了一种全新的概念方法来研究自主架构，除了给信息和安全行业带来好处外，还产生了社会效益。这项提议预计将有助于在加拿大的机器学习领域建立研发领先地位，这对我们信息部门的经济增长至关重要，并对加拿大整体经济和高素质人才的培训(HQP)产生影响。

There is a growing need to develop better understanding of autonomous, distributed, and intelligent systems in terms of interaction, behaviour, and management. It is intriguing to discovering novel ways to address Interacting Cooperating Autonomies (ICA) operating in a collaborative environment, assuming all autonomies aim to satisfy non-contradicting overarching goals. Future autonomies are expected to operate in increasingly complex and hostile environments, respond to growing human needs, and maintain optimal operations with high resilience and adaptability at all times. To achieve those lofty goals, autonomies shall be built over open architectures with embedded tools and algorithms to measure, evaluate, and optimally respond to changes in their environments. Interactions between autonomies need to be measured and evaluated. Exchange of trust between ICA may lead to autonomy isolation if it violates expectations reached by the exchanged trust metric. In order to build, observe, analyze, and interact with future massive intelligent ICAs considering their dynamic, hostile, and complex environments it is important to rethink how we construct and merge modern autonomies. The fundamental concept to realizing the benefits of future ICAs remains in facilitating open, dynamic, and varying architecture where the dynamics of the architecture respond to the dynamics of its environment. Since intelligent solutions tend to deduce intelligence from contextual knowledge, building and analyzing context-awareness frameworks are crucial and vital to the development of ICA models. While there are wide classifications of ICAs, most surveyed collaborating autonomies focus on interaction between identical or almost identical autonomies, this proposal goes beyond this limitation. Further, this proposal remains unique in seeking the value of applying machine learning to the interaction process itself. The principal impact of this proposal is the introduction of a fundamentally new conceptual approach to studying autonomous architecture and yields societal benefits in addition to benefits to the information and security industries. the proposal is expected to help build research and development leadership in the machine learning areas in Canada which is pivotal to the growth of our information sector's economy and has an impact on the Canadian economy in general and the training of Highly Qualified Personnel (HQP).