Russell, Stuart (University of California, Berkeley) | Dietterich, Tom (Oregon State University) | Horvitz, Eric (Microsoft) | Selman, Bart (Cornell University) | Rossi, Francesca (University of Padova) | Hassabis, Demis (DeepMind) | Legg, Shane (DeepMind) | Suleyman, Mustafa (DeepMind) | George, Dileep (Vicarious) | Phoenix, Scott (Vicarious)
The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.
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.
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.