| In recent years, there has been a growing attention towards the development of artificial agents that can naturally communicate and interact with humans. The focus has primarily been on creating systems that have the ability to unify advanced learning algorithms along with various natural forms of human interaction (like providing advice, guidance, motivation, punishment, etc). However, despite the progress made, interactive systems are still directed towards researchers and scientists and consequently the everyday human is unable to exploit the potential of these systems. Another undesirable component is that in most cases, the interacting human is required to communicate with the artificial agent a large number of times, making the human often fatigued. In order to improve these systems, this thesis extends prior work and introduces novel approaches via Human-assisted Efficient Learning Protocols (HELP).;Three case studies are presented that detail distinct aspects of HELP---(a) representation of the task to be learned and its associated constraints, (b) the efficiency of the learning algorithm used by the artificial agent and (c) the unexplored "natural" modes of human interaction. The case studies will show how an artificial agent is able to efficiently learn and perform complex tasks using only a limited number of interactions with a human. Each of these studies involves humans subjects interacting with a real robot and/or simulated agent to learn a particular task. The focus of HELP is to show that a machine can learn better from humans if it is given the ability to take advantage of the knowledge provided by interacting with a human partner or teacher. |
