This paper describes an innovative multiagent system called SAVES with the goal of conserving energy in commercial buildings. We specifically focus on an application to be deployed in an existing university building that provides several key novelties: (i) jointly performed with the university facility management team, SAVES is based on actual occupant preferences and schedules, actual energy consumption and loss data, real sensors and hand-held devices, etc.; (ii) it addresses novel scenarios that require negotiations with groups of building occupants to conserve energy; (iii) it focuses on a non-residential building, where human occupants do not have a direct financial incentive in saving energy and thus requires a different mechanism to effectively motivate occupants; and (iv) SAVES uses a novel algorithm for generating optimal MDP policies that explicitly consider multiple criteria optimization (energy

Computational models of moral cognition will be critical to the creation of agents and robots that operate autonomously in morally sensitive and complex domains. We propose a framework for developing computational models of moral cognition based on behavioral and neurobiological experimental results and field observations. Specifically, we discuss the following critical issues in building such models: 1. Managing conflicts between different moral concerns; 2. The role of moral perceptions in moral judgments; 3. Mechanisms and consequences of moral emotions; 4. Learning and adjusting moral behavior. Moreover, we discuss computational architectures for building and exploring models of moral cognition at different levels of analysis: individual, small groups and large groups. 1.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Authority sharing in human-robot systems

Sustainable energy domains have become extremely important due to the significant growth in energy usage. Building multiagent sys-tems for real-world energy applications raises several research chal-lenges regarding scalability, optimizing multiple competing objec-tives, model uncertainty, and complexity in deploying the system. Motivated by these challenges, this paper proposes a new approach to effectively conserve building energy. This work contributes to a very new area that requires considering large-scale multi-objective optimization as well as uncertainty over occupant preferences when negotiating energy reduction. There are three major contributions. We (i) develop a new method called HRMM to compute robust solutions in practical situations; (ii) experimentally show that ob-tained strategies from HRMM converge to near-optimal solutions; and (iii) provide a systematic way to tightly incorporate the insights from human subject studies into our computational model and al-gorithms. The HRMM method is verified in a validated simulation testbed in terms of energy savings and comfort levels of occupants.

First and foremost, I would like to thank my advisor, Professor Milind Tambe, director of the TEAMCORE research group. When I first joined, I literally had no idea what the definition of a good advisor was, but it did not take too long for me to realize how lucky I was to have chosen to work with Milind. Milind is a great advisor and one of the smartest and most creative people I know. I hope that I can be as lively, enthusiastic, and energetic as him and someday be able to command an audience as well as he can. Milind has been supportive and has given me the freedom to pursue various projects without objection. He has also provided insightful discussions about the research and has taught me new ways of thinking throughout my PhD tenure. In addition to our academic collaboration, I greatly value the close personal support that Milind has provided over the years. Quite simply I cannot imagine a better advisor. Next, I would also like to thank my co-advisor, Professor Pradeep Varakantham at Singapore Management University. He has been a great advisor, mentor, and friend ever since we met at Carnegie Mellon University. I remember the short conversation with Pradeep at CMU has eventually led me to USC and the TEAMCORE research group. I appreciate all of the time and ideas he contributed to make my PhD experience productive and stimulating. The joy and enthusiasm Pradeep has for his research was contagious and motivational for me throughout ii my time at USC. I am also thankful for the excellent example he has provided as a successful researcher and professor. Of course I gratefully acknowledge the other members of my dissertation guidance committee for their time and valuable feedback on my research and thesis. In a line of research at the intersection of many disciplines, my interdisciplinary committee could not have been more perfect for shaping and pushing my research to the heights I have been able to achieve. My sincerest