People often learn from others’ demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demonstration has been less well studied computationally. Here, we develop a Bayesian model for teaching by demonstration. Stark differences arise when demonstrators are intentionally teaching (i.e. showing) a task versus simply pe...

A major unresolved issue in business cycle theory is the construction of an endogenous propagation mechanism capable of capturing the persistence displayed in the data. In this paper we explore the quantitative implications of one propagation mechanism: learning by doing. Estimation of the parameters characterizing learning by doing is based on aggregate, 2-digit and plant level observations in...

Emohawk is a narrative-based serious game designed to be a supportive tool for teaching basics of virtual agents development at universities and high-schools. Emohawk is built utilizing a free version of Unreal Engine 2 and it features an interactive scenario with four virtual agents controlled by an appraisal-driven architecture playing out a story approximately 5-10 minutes long. Students are...

In this paper an approach to learning cognitive skills from problem solving experience is presented – addressing some phenomena well known from human learning but seldom covered together in machine learning. The core of our approach is the acquisition of recursive program schemes (RPSs) by generalization-to-n over plans, using an inductive program synthesis technique. RPSs represent domain spec...

In the first editorial of this series, we described the harms of overscreening, defined as screening patients who are not in the recommended age range or ordering screening tests more often than necessary. Overscreening can exacerbate the problem of overdiagnosis, which is the topic of this editorial. Overdiagnosis occurs when a disease is diagnosed and treated, even though that disease never w...