Technology
AAAI 2008 Workshop Reports
Anand, Sarabjot Singh (University of Warwick) | 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.) | Smith, Trey (Carnegie Mellon University West) | Taylor, Matthew E. (University of Southern California) | Wagstaff, Kiri (Jet Propulsion Laboratory) | Walsh, William (CombineNet) | Zhou, Ron (Palo Alto Research Center)
Preference Handling - An Introductory Tutorial
Brafman, Ronen (Ben-Gurion University) | Domshlak, Carmel
We present a tutorial introduction to the area of preference handling - one of the core issues in the design of any system that automates or supports decision making. The main goal of this tutorial is to provide a framework, or perspective, within which current work on preference handling -representation, reasoning, and elicitation - can be understood. Our intention is not to provide a technical description of the diverse methods used, but rather, to provide a general perspective on the problem and its varied solutions and to highlight central ideas and techniques.
Agents, Bodies, Constraints, Dynamics, and Evolution
Mackworth, Alan K. (University of British Columbia)
The theme of this article is the dynamics of evolution of agents. That theme is applied to the evolution of constraint satisfaction, of agents themselves, of our models of agents, of artificial intelligence and, finally, of the Association for the Advancement of Artificial Intelligence (AAAI). The overall thesis is that constraint satisfaction is central to proactive and responsive intelligent behavior.
The 2008 Scheduling and Planning Applications Workshop (SPARK'08)
Castillo, Luis (University of Granada) | Cortellessa, Gabriella (ISTC-CNR) | Yorke-Smith, Neil (SRI International)
SPARK'08 was the first edition of a workshop series designed to provide a stable, long-term forum where researchers could discuss the applications of planning and scheduling techniques to real problems. Animated discussion characterized the workshop, which was collocated with Eighteenth International Conference on Automated Planning and Scheduling (ICAPS-08) held in Sydney, Australia in September 2008.
AAAI-08 and IAAI-08 Conferences Provide Focal Point for AI
Hedberg, Sara Reese (Emergent, In.c)
This summer's AAAI Conference on Artificial Intelligence (AAAI-08) and its sister Conference on Innovative Applications of AI (IAAI-08) continued their long tradition of being a focal point of AI. This year's conferences were held in Chicago at the Hyatt Regency McCormick Place, July 13-17, 2008. The multidimensional conference offerings included nine invited talks, 251 technical papers, 22 innovative applications of AI papers, three competitions (poker, AI video, and general game playing), three special tracks (AI and the web, integrated intelligence, and physically grounded AI), 15 tutorials, 15 workshops, and 11 intelligent system demonstrations, as well as a number of awards, a doctoral consortium, student poster session and programs, and a vendor exhibit. An additional 175 people exclusively attended the tutorials, workshops, or exhibit.
The Seventeenth Annual AAAI Robot Exhibition and Manipulation and Mobility Workshop
Anderson, Monica (The University of Alabama) | Jenkins, Odest Chadwicke (Brown University) | Oh, Paul (Drexel University)
The AAAI 2008 Workshop on Mobility and Manipulation (held during the Twenty-Third AAAI Conference on Artificial Intelligence) showcased advances in mobility and manipulation through a half-day workshop and an exhibition. The workshop focused on possible solutions to both technical and organizational challenges to mobility and manipulation research. This article presents the highlights of that discussion along with the content of the accompanying exhibits.
Report on the Fourth International Conference on Knowledge Capture (K-CAP 2007)
Sleeman, Derek (University of Aberdeen) | Barker, Ken (University of Texas) | Corsar, David (University of Aberdeen)
The Fourth International Conference on Knowledge Capture was held October 28-31, 2007 in Whistler, British Columbia. K-CAP 2007 included two invited talks, technical papers, posters, and demonstrations. Topics included knowledge engineering and modeling methodologies, knowledge engineering and the semantic web, mixed-initiative planning and decision-support tools, acquisition of problem-solving knowledge, knowledge-based markup techniques, knowledge extraction systems, knowledge acquisition tools, and advice taking systems.
AAAI-08 and IAAI-08 Conferences Provide Focal Point for AI
Hedberg, Sara Reese (Emergent, In.c)
This year's conferences were held in Perhaps one of the true litmus tests of any conference is the caliber of the invited speakers. Sensibility: Sentiment Analysis, Opinion and research manager at Microsoft Research) The distinguished Robert S. Englemore Mining, and the Computational who gave his AAAI presidential Memorial Award Lecture was delivered Treatment of Subjective Language"), address, "Artificial Intelligence in the by Kenneth Ford (Florida Institute while Seth C. Goldstein (Carnegie Open World." Mel lon University) discussed revolutionary Chris Urmson (Carnegie Mellon In his lecture, "Toward Cognitive work in self-reconfiguring programmable University), a leading member of the Prostheses," Ford discussed human-centered matter composed of ensembles of submillimeter robots in his DARPA Urban Grand Challenge winning computing to amplify talk, "Realizing Claytronics: A Challenge team, described the race and winning human cognition and perception. Instead of the learning for network analysis in ("From Images to Scenes: Using popular competition, which has his talk, "Making Sense of Complex Lots of Data to Infer Geometric, Photometric, pushed the envelope of mobile robotics Networks." David Haussler (University and Semantic Scene Properties since its inception, this year was of California, Santa Cruz) traced the from a Single Image"), and Lillian host to a Robot Workshop and Exhibition.
Monte Carlo Sampling Methods for Approximating Interactive POMDPs
Doshi, P., Gmytrasiewicz, P. J
Partially observable Markov decision processes (POMDPs) provide a principled framework for sequential planning in uncertain single agent settings. An extension of POMDPs to multiagent settings, called interactive POMDPs (I-POMDPs), replaces POMDP belief spaces with interactive hierarchical belief systems which represent an agent's belief about the physical world, about beliefs of other agents, and about their beliefs about others' beliefs. This modification makes the difficulties of obtaining solutions due to complexity of the belief and policy spaces even more acute. We describe a general method for obtaining approximate solutions of I-POMDPs based on particle filtering (PF). We introduce the interactive PF, which descends the levels of the interactive belief hierarchies and samples and propagates beliefs at each level. The interactive PF is able to mitigate the belief space complexity, but it does not address the policy space complexity. To mitigate the policy space complexity - sometimes also called the curse of history - we utilize a complementary method based on sampling likely observations while building the look ahead reachability tree. While this approach does not completely address the curse of history, it beats back the curse's impact substantially. We provide experimental results and chart future work.