Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …

Decision analysis deals with modeling and enhancing decision processes. A principal
challenge in improving behavior is in obtaining a transparent* description* of existing …

Consider learning a policy purely on the basis of demonstrated behavior---that is, with no
access to reinforcement signals, no knowledge of transition dynamics, and no further …

Abstract Inverse Reinforcement Learning (IRL) describes the problem of learning an
unknown reward function of a Markov Decision Process (MDP) from observed behavior of …

Understanding human behavior from observed data is critical for transparency and
accountability in decision-making. Consider real-world settings such as healthcare, in which …

Inverse reinforcement learning (IRL) aims to explain observed strategic behavior by fitting
reinforcement learning models to behavioral data. However, traditional IRL methods are …

In this work, we present our various contributions to the objective of building a decision
support tool for the diagnosis of rare diseases. Our goal is to achieve a state of knowledge …

On the level of decision support, most algorithmic problems encountered in machine
learning are instances of pure prediction or pure automation tasks. This dissertation takes a …

Summary The field of General Game Playing (GGP) usually yearns to create agents that are
able to win any game as efficiently as possible. Utility functions are then easy to find …

This project evaluates the modularity of a recent Bayesian Inverse Reinforcement Learning
approach [1] by inferring the sub-goals correlated with winning board games from …