Human-Machine Inference Networks For Smart Decision Making: Opportunities and Challenges

ABSTRACT The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves. While inference performance optimization techniques for human-only or sensor-only networks are quite mature, HuMaINs require novel signal processing and machine learning solutions. In this paper, we present an overview of the HuMaINs architecture with a focus on three main issues that include architecture design, inference algorithms including security/privacy challenges, and application areas/use cases. Index Terms-- human-in-the-loop systems, behavioral signal processing, self-driving cars, health care informatics, intelligent tutoring systems 1. INTRODUCTION In traditional economics, cognitive psychology, and artificial intelligence (AI) literature, the problem-solving or inference process is described in terms of searching a problem space, which consists of various states of the problem, starting with the initial state and ending at the goal state which one would like to reach [1]. Each path from the initial state represents a possible strategy which can be used.