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.
desJardins, Marie (University of Maryland Baltimore County)
his column describes my experience with using a new classroom space (the ACTIVE Center), which was designed to facilitate group-based active learning and problem solving, to teach an introductory artificial intelligence course. By restructuring the course into a format that was roughly half lecture and half small-group problem-solving, I was able to significantly increase student engagement, their understanding and retention of difficult concepts, and my own enjoyment in teaching the class.
We describe an application of AI search and information visualization techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different trade-offs in the decision space. We show the resulting plans using novel visualization methods that we have developed for summarizing and comparing alternative plans.
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.
Wilkins, David E., desJardins, Marie
We are interested in solving real-world planning problems and, to that end, argue for the use of domain knowledge in planning. We believe that the field must develop methods capable of using rich knowledge models to make planning tools useful for complex problems. We discuss the suitability of current planning paradigms for solving these problems. Finally, we draw an analogy from the current focus of the planning community on disjunctive planners to the experiences of the machine learning community over the past decade.
desJardins, Marie, Wolverton, Michael
Distributed SIPE (DSIPE) is a distributed planning system that provides decision support to human planners in a collaborative planning environment. The key contributions of our research on DSIPE are (1) constraint-based, consistent local views of the global plan that give each planner a view of how other planners' subplans relate to their local planning decisions; (2) methods for automatically identifying and sharing potentially relevant information among distributed planning agents; and (3) techniques for merging subplans that leverage the shared subplan structure to generate a complete, final plan. DSIPE is a fully implemented system and has been demonstrated to end users in the maritime (United States Navy and United States Marine Corps) planning community.
The Association for the Advancement of Artificial Intelligence (AAAI) held its 1998 Fall Symposium Series on 23 to 25 October at the Omni Rosen Hotel in Orlando, Florida. This article contains summaries of seven of the symposia that were conducted: (1) Cognitive Robotics; (2) Distributed, Continual Planning; (3) Emotional and Intelligent: The Tangled Knot of Cognition; (4) Integrated Planning for Autonomous Agent Architectures; (5) Planning with Partially Observable Markov Decision Processes; (6) Reasoning with Visual and Diagrammatic Representations; and (7) Robotics and Biology: Developing Connections.
Woods, William, Uckun, Sendar, Kohane, Isaac, Bates, Joseph, Hulthage, Ingemar, Gasser, Les, Hanks, Steve, Gini, Maria, Ram, Ashwin, desJardins, Marie, Johnson, Peter, Etzioni, Oren, Coombs, David, Whitehead, Steven
The Association for the Advancement of Artificial Intelligence (AAAI) held its 1994 Spring Symposium Series on 19-23 March at Stanford University, Stanford, California. This article contains summaries of 10 of the 11 symposia that were conducted: Applications of Computer Vision in Medical Image Processing; AI in Medicine: Interpreting Clinical Data; Believable Agents; Computational Organization Design; Decision-Theoretic Planning; Detecting and Resolving Errors in Manufacturing Systems; Goal-Driven Learning; Intelligent Multimedia, Multimodal Systems; Software Agents; and Toward Physical Interaction and Manipulation. Papers of most of the symposia are available as technical reports from AAAI.
My Ph.D. dissertation describes PAGODA (probabilistic autonomous goal-directed agent), a model for an intelligent agent that learns autonomously in domains containing uncertainty. The ultimate goal of this line of research is to develop intelligent problem-solving and planning systems that operate in complex domains, largely function autonomously, use whatever knowledge is available to them, and learn from their experience. PAGODA was motivated by two specific requirements: The agent should be capable of operating with minimal intervention from humans, and it should be able to cope with uncertainty (which can be the result of inaccurate sensors, a nondeterministic environment, complexity, or sensory limitations). I argue that the principles of probability theory and decision theory can be used to build rational agents that satisfy these requirements.