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.
Recent years have seen significant technical progress on robot planning, enabling robots to compute actions and motions to accomplish challenging tasks involving driving, flying, walking, or manipulating objects. However, robots that have been commercially deployed in the real world typically have no or minimal planning capability. Although these robots are highly successful in their respective niches, a lack of planning capabilities limits the range of tasks for which currently deployed robots can be used. In this article, we highlight key conclusions from a workshop sponsored by the National Science Foundation in October 2013 that summarize opportunities and key challenges in robot planning and include challenge problems identified in the workshop that can help guide future research towards making robot planning more deployable in the real world.
Morris, Robert (NASA) | Bonet, Blai (Universidad Simón Bolívar) | Cavazza, Marc (Teesside University) | desJardins, Marie (University of Maryland, Baltimore County) | Felner, Ariel (BenGurion University) | Hawes, Nick (University of Birmingham) | Knox, Brad (Massachusetts Institute of Technology) | Koenig, Sven (University of Southern California) | Konidaris, George (Massachusetts Institute of Technology,) | Lang, Jérôme ((Université ParisDauphine) | López, Carlos Linares (Universidad Carlos III de Madrid) | Magazzeni, Daniele (King's College London) | McGovern, Amy (University of Oklahoma) | Natarajan, Sriraam (Indiana University) | Sturtevant, Nathan R. (University of Denver,) | Thielscher, Michael (University New South Wales) | Yeoh, William (New Mexico State University) | Sardina, Sebastian (RMIT University) | Wagstaff, Kiri (Jet Propulsion Laboratory)
The Twenty-Ninth AAAI Conference on Artificial Intelligence, (AAAI-15) was held in January 2015 in Austin, Texas (USA) The conference program was cochaired by Sven Koenig and Blai Bonet. This report contains reflective summaries of the main conference, the robotics program, the AI and robotics workshop, the virtual agent exhibition, the what's hot track, the competition panel, the senior member track, student and outreach activities, the student abstract and poster program, the doctoral consortium, the women's mentoring event, and the demonstrations program.
In robotics and video games, one often discretizes continuous terrain into a grid with blocked and unblocked grid cells and then uses path-planning algorithms to find a shortest path on the resulting grid graph. This path, however, is typically not a shortest path in the continuous terrain. In this overview article, we discuss a path-planning methodology for quickly finding paths in continuous terrain that are typically shorter than shortest grid paths. Any-angle path-planning algorithms are variants of the heuristic path-planning algorithm A* that find short paths by propagating information along grid edges (like A*, to be fast) without constraining the resulting paths to grid edges (unlike A*, to find short paths).
Bunescu, Razvan C (Ohio University) | Carvalho, Vitor R. (Microsoft Live Labs) | Chomicki, Jan (University of Buffalo) | Conitzer, Vincent (Duke University) | Cox, Michael T. (BBN Technologies) | Dignum, Virginia (Utrecht University) | Dodds, Zachary (Harvey Mudd College) | Dredze, Mark (University of Pennsylvania) | Furcy, David (University of Wisconsin Oshkosh) | Gabrilovich, Evgeniy (Yahoo! Research) | Göker, Mehmet H. (PricewaterhouseCoopers) | Guesgen, Hans Werner (Massey University) | Hirsh, Haym (Rutgers University) | Jannach, Dietmar (Dortmund University of Technology) | Junker, Ulrich (ILOG) | Ketter, Wolfgang (Erasmus University) | Kobsa, Alfred (University of California, Irvine) | Koenig, Sven (University of Southern California) | Lau, Tessa (IBM Almaden Research Center) | Lewis, Lundy (Southern New Hampshire University) | Matson, Eric (Purdue University) | Metzler, Ted (Oklahoma City University) | Mihalcea, Rada (University of North Texas) | Mobasher, Bamshad (DePaul University) | Pineau, Joelle (McGill University) | Poupart, Pascal (University of Waterloo) | Raja, Anita (University of North Carolina at Charlotte) | Ruml, Wheeler (University of New Hampshire) | Sadeh, Norman M. (Carnegie Mellon University) | Shani, Guy (Microsoft Research) | Shapiro, Daniel (Applied Reactivity, Inc.) | Singh, Sarabjot Anand (University of Warwick) | Taylor, Matthew E. (University of Southern California) | Wagstaff, Kiri (Jet Propulsion Laboratory) | Smith, Trey (Carnegie Mellon University West) | Walsh, William (CombineNet) | Zhou, Ron (Palo Alto Research Center)
The program included the following fifteen workshops: Advancements in POMDP Solvers, AI Education Workshop, Coordination, Organization, Institutions and Norms in Agent Systems, Enhanced Messaging, Human Implications of Human-Robot Interaction, Intelligent Techniques for Web Personalization and Recommender Systems, Metareasoning: Thinking about Thinking, Multidisciplinary Workshop on Advances in Preference Handling, Search in Artificial Intelligence and Robotics, Spatial and Temporal Reasoning, Trading Agent Design and Analysis, Transfer Learning for Complex Tasks, What Went Wrong and Why: Lessons from AI Research and Applications, and Wikipedia and Artificial Intelligence: An Evolving Synergy. The goal of the Coordination, Organizations, Institutions and Norms in Multiagent Systems workshop was to examine and define the current state of the art research in agent systems research related to coordination, organizations institutions and norming. The Intelligent Techniques for Web Personalization and Recommender Systems workshop was scheduled as a joint event, bringing together researchers and practitioners from the fields of web personalization and recommender systems. The Search in Artificial Intelligence and Robotics workshop brought together search researchers to share their ideas and disseminate their latest research results.
Achtner, Wolfgang, Aimeur, Esma, Anand, Sarabjot Singh, Appelt, Doug, Ashish, Naveen, Barnes, Tiffany, Beck, Joseph E., Dias, M. Bernardine, Doshi, Prashant, Drummond, Chris, Elazmeh, William, Felner, Ariel, Freitag, Dayne, Geffner, Hector, Geib, Christopher W., Goodwin, Richard, Holte, Robert C., Hutter, Frank, Isaac, Fair, Japkowicz, Nathalie, Kaminka, Gal A., Koenig, Sven, Lagoudakis, Michail G., Leake, David B., Lewis, Lundy, Liu, Hugo, Metzler, Ted, Mihalcea, Rada, Mobasher, Bamshad, Poupart, Pascal, Pynadath, David V., Roth-Berghofer, Thomas, Ruml, Wheeler, Schulz, Stefan, Schwarz, Sven, Seneff, Stephanie, Sheth, Amit, Sun, Ron, Thielscher, Michael, Upal, Afzal, Williams, Jason, Young, Steve, Zelenko, Dmitry
The Workshop program of the Twenty-First Conference on Artificial Intelligence was held July 16-17, 2006 in Boston, Massachusetts. The program was chaired by Joyce Chai and Keith Decker. The titles of the 17 workshops were AIDriven Technologies for Service-Oriented Computing; Auction Mechanisms for Robot Coordination; Cognitive Modeling and Agent-Based Social Simulations, Cognitive Robotics; Computational Aesthetics: Artificial Intelligence Approaches to Beauty and Happiness; Educational Data Mining; Evaluation Methods for Machine Learning; Event Extraction and Synthesis; Heuristic Search, Memory- Based Heuristics, and Their Applications; Human Implications of Human-Robot Interaction; Intelligent Techniques in Web Personalization; Learning for Search; Modeling and Retrieval of Context; Modeling Others from Observations; and Statistical and Empirical Approaches for Spoken Dialogue Systems.
The 2005 Autonomous Agents and Multiagent Systems Conference (AAMAS 2005) was held July 25-29, 2005, at the University of Utrecht, the Netherlands. This report reviews the activities of that conference, including the workshop and tutorial programs, the main conference and poster tracks, the industry paper track, the demonstration track and sponsor demonstration sessions, the invited talks, exhibition, doctoral mentoring program, as well the sponsorship and scholarships activities.
The Fourteenth International Conference on Automated Planning and Scheduling (ICAPS-04) was held in Canada in June of 2004. It covered the latest theoretical and empirical advances in planning and scheduling. The conference program consisted of tutorials, workshops, a doctoral consortium, and three days of technical paper presentations in a single plenary track, one day of which was jointly organized with the Ninth International Conference on Principles of Knowledge Representation and Reasoning. ICAPS-04 also hosted the International Planning Competition, including a classical track and a newly formed probabilistic track.
Incremental search reuses information from previous searches to find solutions to a series of similar search problems potentially faster than is possible by solving each search problem from scratch. This is important because many AI systems have to adapt their plans continuously to changes in (their knowledge of) the world. In this article, we give an overview of incremental search, focusing on LIFELONG PLANNING A*, and outline some of its possible applications in AI.
In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. Agent-centered search methods interleave planning and plan execution and restrict planning to the part of the domain around the current state of the agent, for example, the current location of a mobile robot or the current board position of a game. These methods can execute actions in the presence of time constraints and often have a small sum of planning and execution cost, both because they trade off planning and execution cost and because they allow agents to gather information early in nondeterministic domains, which reduces the amount of planning they have to perform for unencountered situations. Agent-centered search methods have been applied to a variety of domains, including traditional search, strips-type planning, moving-target search, planning with totally and partially observable Markov decision process models, reinforcement learning, constraint satisfaction, and robot navigation.