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Towards a learning-algorithm agnostic generative policy model for coalitions
Author(s): Daniel Cunnington; Mark Law; Geeth de Mel; Irene Manotas; Elisa Bertino; Seraphin Calo; Dinesh Verma
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Paper Abstract

Autonomous systems are expected to have a major impact in future coalition operations. These systems are enabled by a variety of Artificial Intelligence (AI) learning algorithms that contextualize and adapt in varying, possibly unforeseen situations to assist humans in achieving complex tasks. Moreover, these systems will be required to operate in dynamic and challenging environments that impose certain constraints such as task formation and collaboration, ad-hoc resource availability, rapidly changing environmental conditions and the requirement to abide by mission objectives. Therefore, such systems require the capability to adapt and evolve so that they can behave autonomously at the edge of the network in new situations. Crucially, autonomous systems have to understand the bounds in which they can operate based on their own capability and the constraints of the environment. Policies are typically used by systems to define their behavior and constraints and often these policies are manually configured and managed by humans. AI-enabled systems will require novel approaches to rapidly learn, create, augment, and model emerging policies to support real-time decision making. Recent work has shown that such policy model generations are possible through symbolic learning to shallow and deep learning approaches for different classes of problems. Motivated by this observation, in this paper, we propose to apply recent advances in explainable-AI to develop an approach which is agnostic to the learning algorithm, thus enabling seamless policy generation in the coalition environment.

Paper Details

Date Published: 10 May 2019
PDF: 15 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060J (10 May 2019); doi: 10.1117/12.2520243
Show Author Affiliations
Daniel Cunnington, IBM United Kingdom Ltd. (United Kingdom)
Mark Law, Imperial College London (United Kingdom)
Geeth de Mel, IBM United Kingdom Ltd. (United Kingdom)
Irene Manotas, IBM Thomas J. Watson Research Ctr. (United States)
Elisa Bertino, Purdue Univ. (United States)
Seraphin Calo, IBM Thomas J. Watson Research Ctr. (United States)
Dinesh Verma, IBM Thomas J. Watson Research Ctr. (United States)


Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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