Rethinking Intelligent Behavior as Competitive Games for Handling Adversarial Challenges to Machine Learning

Adversarial machine learning necessitates revisiting conventional machine learning paradigms and how they embody intelligent behavior. Effective repudiation of adversarial challenges adds a new dimension to intelligent behavior above and beyond that exemplified by widely used machine learning techniques such as supervised learning. For a learner to be resistant to adversarial attack, it must have two capabilities: a primary capability that it performs normally; and a second capability of resistance to attacks from adversaries. A possible means to achieve the second capability is to develop an understanding of different attack related attributes such as who generates attacks and why, how and when attacks are generated, and, what previously unseen attacks might look like. We trace the idea that this involves an additional dimension of intelligent behavior to the basic structure by which the problem may be solved. We posit that modeling this scenario as competitive, multi-player games comprising strategic interactions between different players with contradictory and competing objectives provides a systematic and structured means towards understanding and analyzing the problem. Exploring further in this direction, we discuss relevant features of different multi-player gaming environments that are being investigated as research platforms for addressing open problems and challenges towards developing artificial intelligence algorithms that are capable of super human intelligence.