Grant: Discovery Grants Program - Individual

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).