Innovative reasoning and emergent learning

Can we teach computers to think outside the box? In other words, is it possible to replicate the innovative decision process computationally for machine learning? Extant research in machine learning has typically either focused on (a) building predictive models of a single internally-consistent target or on (b) a single task or decision in isolation, and more rarely, both, given the difficulties already posed within these more restrictive problems. The successes and utility of modern machine learning is clearly evident in numerous applications across many domains, and ever more so now with Big Data. Yet, the former focus (a) has made machine learning of complex targets (e.g. systems of systems, complex systems, a human) very elusive because of their inherent assumptions of expectations. This precludes the ability to learn emergent, unexpected, or innovative behaviors. (See also Nonlinear decision-making)

Faculty contact: Eugene Santos Jr.