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主、分布式和智能系统。这是有趣的发现新的方法来解决互动合作自治（伊卡）在协作环境中运行，假设所有的企业都旨在满足非矛盾的总体目标。未来的企业预计将在日益复杂和恶劣的环境中运营，应对日益增长的人类需求，并始终保持高弹性和适应性的最佳运营。为了实现这些崇高的目标，企业应该建立在开放式架构之上，并使用嵌入式工具和算法来测量、评估和优化其环境中的变化。需要衡量和评价企业之间的相互作用。伊卡之间的信任交换可能会导致自治隔离，如果它违反了交换的信任度量所达到的期望。考虑到未来大规模智能ICA的动态、恶劣和复杂的环境，为了构建、观察、分析和与之交互，重新思考我们如何构建和合并现代智能系统是很重要的。实现未来ICA好处的基本概念仍然是促进开放、动态和变化的架构，其中架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伊卡模型的开发至关重要。虽然国际商品协定的分类很广，但大多数接受调查的合作企业侧重于相同或几乎相同的企业之间的互动，本提案超越了这一限制。此外，该提案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。这一提议的主要影响是引入了一种全新的概念方法来研究自主架构，并在信息和安全行业之外产生社会效益。该提案预计将有助于在加拿大机器学习领域建立研发领导地位，这对我们信息部门的经济增长至关重要，并对加拿大经济和高素质人才（HQP）的培训产生影响。

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).