Burton, Emanuelle (University of Kentucky) | Goldsmith, Judy (University of Kentucky) | Koenig, Sven (University of Southern California) | Kuipers, Benjamin (University of Michigan) | Mattei, Nicholas (IBM Research) | Walsh, Toby (University of New South Wales and Data61)
The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for use by AI educators. We also provide concrete suggestions on how to integrate AI ethics into a general artificial intelligence course and how to teach a stand-alone artificial intelligence ethics course.
Albrecht, Stefano (The University of Texas at Austin) | Bouchard, Bruno (Université du Québec à Chicoutimi) | Brownstein, John S. (Harvard University) | Buckeridge, David L. (McGill University) | Caragea, Cornelia (University of North Texas) | Carter, Kevin M. (MIT Lincoln Laboratory) | Darwiche, Adnan (University of California, Los Angeles) | Fortuna, Blaz (Bloomberg L.P. and Jozef Stefan Institute) | Francillette, Yannick (Université du Québec à Chicoutimi) | Gaboury, Sébastien (Université du Québec à Chicoutimi) | Giles, C. Lee (Pennsylvania State University) | Grobelnik, Marko (Jozef Stefan Institute) | Hruschka, Estevam R. (Federal University of São Carlos) | Kephart, Jeffrey O. (IBM Thomas J. Watson Research Center) | Kordjamshidi, Parisa (University of Illinois at Urbana-Champaign) | Lisy, Viliam (University of Alberta) | Magazzeni, Daniele (King's College London) | Marques-Silva, Joao (University of Lisbon) | Marquis, Pierre (Université d'Artois) | Martinez, David (MIT Lincoln Laboratory) | Michalowski, Martin (Adventium Labs) | Shaban-Nejad, Arash (University of California, Berkeley) | Noorian, Zeinab (Ryerson University) | Pontelli, Enrico (New Mexico State University) | Rogers, Alex (University of Oxford) | Rosenthal, Stephanie (Carnegie Mellon University) | Roth, Dan (University of Illinois at Urbana-Champaign) | Sinha, Arunesh (University of Southern California) | Streilein, William (MIT Lincoln Laboratory) | Thiebaux, Sylvie (The Australian National University) | Tran, Son Cao (New Mexico State University) | Wallace, Byron C. (University of Texas at Austin) | Walsh, Toby (University of New South Wales and Data61) | Witbrock, Michael (Lucid AI) | Zhang, Jie (Nanyang Technological University)
The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) was held at the beginning of the conference, February 12-13, 2016. Workshop participants met and discussed issues with a selected focus -- providing an informal setting for active exchange among researchers, developers and users on topics of current interest. To foster interaction and exchange of ideas, the workshops were kept small, with 25-65 participants. Attendance was sometimes limited to active participants only, but most workshops also allowed general registration by other interested individuals.
Albrecht, Stefano V. (University of Edinburgh) | Beck, J. Christopher (University of Toronto) | Buckeridge, David L. (McGill University) | Botea, Adi (IBM Research, Dublin) | Caragea, Cornelia (University of North Texas) | Chi, Chi-hung (Commonwealth Scientific and Industrial Research Organisation) | Damoulas, Theodoros (New York University) | Dilkina, Bistra (Georgia Institute of Technology) | Eaton, Eric (University of Pennsylvania) | Fazli, Pooyan (Carnegie Mellon University) | Ganzfried, Sam (Carnegie Mellon University) | Giles, C. Lee (Pennsylvania State University) | Guillet, Sébastian (Université du Québec) | Holte, Robert (University of Alberta) | Hutter, Frank (University of Freiburg) | Koch, Thorsten (TU Berlin) | Leonetti, Matteo (University of Texas at Austin) | Lindauer, Marius (University of Freiburg) | Machado, Marlos C. (University of Alberta) | Malitsky, Yui (IBM Research) | Marcus, Gary (New York University) | Meijer, Sebastiaan (KTH Royal Institute of Technology) | Rossi, Francesca (University of Padova, Italy) | Shaban-Nejad, Arash (University of California, Berkeley) | Thiebaux, Sylvie (Australian National University) | Veloso, Manuela (Carnegie Mellon University) | Walsh, Toby (NICTA) | Wang, Can (Commonwealth Scientific and Industrial Research Organisation) | Zhang, Jie (Nanyang Technological University) | Zheng, Yu (Microsoft Research)
AAAI's 2015 Workshop Program was held Sunday and Monday, January 25–26, 2015 at the Hyatt Regency Austin Hotel in Austion, Texas, USA. The AAAI-15 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. Most workshops were held on a single day. The titles of the workshops included AI and Ethics, AI for Cities, AI for Transportation: Advice, Interactivity and Actor Modeling, Algorithm Configuration, Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Beyond the Turing Test, Computational Sustainability, Computer Poker and Imperfect Information, Incentive and Trust in E-Communities, Multiagent Interaction without Prior Coordination, Planning, Search, and Optimization, Scholarly Big Data: AI Perspectives, Challenges, and Ideas, Trajectory-Based Behaviour Analytics, World Wide Web and Public Health Intelligence, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, and Learning for General Competency in Video Games.
We review constraint-based approaches to handle preferences. We start by defining the main notions of constraint programming, then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adapted to handle other forms of preferences, such as bipolar, qualitative, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation generation, machine learning, and preference elicitation, can be useful in modelling and solving soft constraints.
I consider how to represent and reason with users' preferences. While areas of economics like social choice and game theory have traditionally considered such topics, I will argue that computer science and artificial intelligence bring some fresh perspectives to the study of representing and reasoning with preferences. For instance, I consider how we can elicit preferences efficiently and effectively.
Blake, Brian, Haigh, Karen, Hexmoor, Henry, Falcone, Rino, Soh, Leen-Kiat, Baral, Chitta, McIlraith, Sheila, Gmytrasiewicz, Piotr, Parsons, Simon, Malaka, Rainer, Krueger, Antonio, Bouquet, Paolo, Smart, Bill, Kurumantani, Koichi, Pease, Adam, Brenner, Michael, desJardins, Marie, Junker, Ulrich, Delgrande, Jim, Doyle, Jon, Rossi, Francesca, Schaub, Torsten, Gomes, Carla, Walsh, Toby, Guo, Haipeng, Horvitz, Eric J., Ide, Nancy, Welty, Chris, Anger, Frank D., Guegen, Hans W., Ligozat, Gerald
The Association for the Advancement of Artificial Intelligence (AAAI) presented the AAAI-02 Workshop Program on Sunday and Monday, 28-29 July 2002 at the Shaw Convention Center in Edmonton, Alberta, Canada. The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The workshops were Agent-Based Technologies for B2B Electronic-Commerce; Automation as a Caregiver: The Role of Intelligent Technology in Elder Care; Autonomy, Delegation, and Control: From Interagent to Groups; Coalition Formation in Dynamic Multiagent Environments; Cognitive Robotics; Game-Theoretic and Decision-Theoretic Agents; Intelligent Service Integration; Intelligent Situation-Aware Media and Presentations; Meaning Negotiation; Multiagent Modeling and Simulation of Economic Systems; Ontologies and the Semantic Web; Planning with and for Multiagent Systems; Preferences in AI and CP: Symbolic Approaches; Probabilistic Approaches in Search; Real-Time Decision Support and Diagnosis Systems; Semantic Web Meets Language Resources; and Spatial and Temporal Reasoning.
The Association for the Advancement of Artificial Intelligence held its 2001 Fall Symposium Series November 2-4, 2001 at the Sea Crest Conference Center in North Falmouth, Massachusetts. The topics of the five symposia in the 2001 Fall Symposia Series were (1) Anchoring Symbols to Sensor Data in Single and Multiple Robot Systems, (2) Emotional and Intelligent II: The Tangled Knot of Social Cognition, (3) Intent Inference for Collaborative Tasks, (4) Negotiation Methods for Autonomous Cooperative Systems, and (5) Using Uncertainty within Computation. This article contains brief reports of those five symposia.
In the last few years, we have witnessed a major growth in the use of empirical methods in AI. In part, this growth has arisen from the availability of fast networked computers that allow certain problems of a practical size to be tackled for the first time. There is also a growing realization that results obtained empirically are no less valuable than theoretical results. I identify some of the emerging trends in this area by describing a recent workshop that brought together researchers using empirical methods as far apart as robotics and knowledge-based systems.