Agmon, Noa (University of Texas at Austin) | Agrawal, Vikas (Infosys Labs) | Aha, David W. (Naval Research Laboratory) | Aloimonos, Yiannis (University of Maryland, College Park) | Buckley, Donagh (EMC) | Doshi, Prashant (University of Georgia) | Geib, Christopher (University of Edinburgh) | Grasso, Floriana (University of Liverpool) | Green, Nancy (University of North Carolina Greensboro) | Johnston, Benjamin (University of Technology, Sydney) | Kaliski, Burt (VeriSign, Inc.) | Kiekintveld, Christopher (University of Texas at El Paso) | Law, Edith (Carnegie Mellon University) | Lieberman, Henry (Massachusetts Institute of Technology) | Mengshoel, Ole J. (Carnegie Mellon University) | Metzler, Ted (Oklahoma City University) | Modayil, Joseph (University of Alberta) | Oard, Douglas W. (University of Maryland, College Park) | Onder, Nilufer (Michigan Technological University) | O'Sullivan, Barry (University College Cork) | Pastra, Katerina (Cognitive Systems Research Insitute) | Precup, Doina (McGill University) | Ramachandran, Sowmya (Stottler Henke Associates, Inc.) | Reed, Chris (University of Dundee) | Sariel-Talay, Sanem (Istanbul Technical University) | Selker, Ted (Carnegie Mellon University) | Shastri, Lokendra (Infosys Technologies Ltd.) | Smith, Stephen F. (Carnegie Mellon University) | Singh, Satinder (University of Michigan at Ann Arbor) | Srivastava, Siddharth (University of Wisconsin, Madison) | Sukthankar, Gita (University of Central Florida) | Uthus, David C. (Naval Research Laboratory) | Williams, Mary-Anne (University of Technology, Sydney)
The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.
Klenk, Matthew (Navy Center for Applied Research in Artificial Intelligence) | Aha, David W. (Navy Center for Applied Research in Artificial Intelligence) | Molineaux, Matt (Knexus Research Corporation)
Transfer learning occurs when, after gaining experience from learning how to solve source problems, the same learner exploits this experience to improve performance and/or learning on target problems. In transfer learning, the differences between the source and target problems characterize the transfer distance. CBR can support transfer learning methods in multiple ways. We illustrate how CBR and transfer learning interact and characterize three approaches for using CBR in transfer learning: (1) as a transfer learning method, (2) for problem learning, and (3) to transfer knowledge between sets of problems.
Aha, David W. (Naval Research Laboratory) | Boddy, Mark (Adventium Labs) | Bulitko, Vadim (University of Alberta) | Garcez, Artur S. d'Avila (City University London) | Doshi, Prashant (University of Georgia) | Edelkamp, Stefan (TZI, Bremen University) | Geib, Christopher (University of Edinburgh) | Gmytrasiewicz, Piotr (University of Illinois, Chicago) | Goldman, Robert P. (Smart Information Flow Technologies) | Hitzler, Pascal (Wright State University) | Isbell, Charles (Georgia Institute of Technology) | Josyula, Darsana (University of Maryland, College Park) | Kaelbling, Leslie Pack (Massachusetts Institute of Technology) | Kersting, Kristian (University of Bonn) | Kunda, Maithilee (Georgia Institute of Technology) | Lamb, Luis C. (Universidade Federal do Rio Grande do Sul (UFRGS)) | Marthi, Bhaskara (Willow Garage) | McGreggor, Keith (Georgia Institute of Technology) | Nastase, Vivi (EML Research gGmbH) | Provan, Gregory (University College Cork) | Raja, Anita (University of North Carolina, Charlotte) | Ram, Ashwin (Georgia Institute of Technology) | Riedl, Mark (Georgia Institute of Technology) | Russell, Stuart (University of California, Berkeley) | Sabharwal, Ashish (Cornell University) | Smaus, Jan-Georg (University of Freiburg) | Sukthankar, Gita (University of Central Florida) | Tuyls, Karl (Maastricht University) | Meyden, Ron van der (University of New South Wales) | Halevy, Alon (Google, Inc.) | Mihalkova, Lilyana (University of Maryland) | Natarajan, Sriraam (University of Wisconsin)
The AAAI-10 Workshop program was held Sunday and Monday, July 11–12, 2010 at the Westin Peachtree Plaza in Atlanta, Georgia. The AAAI-10 workshop program included 13 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Fun, Bridging the Gap between Task and Motion Planning, Collaboratively-Built Knowledge Sources and Artificial Intelligence, Goal-Directed Autonomy, Intelligent Security, Interactive Decision Theory and Game Theory, Metacognition for Robust Social Systems, Model Checking and Artificial Intelligence, Neural-Symbolic Learning and Reasoning, Plan, Activity, and Intent Recognition, Statistical Relational AI, Visual Representations and Reasoning, and Abstraction, Reformulation, and Approximation. This article presents short summaries of those events.
Dynamic scripting is a reinforcement learning approach to adaptive game AI that learns, during gameplay, which game tactics an opponent should select to play effectively. We introduce the evolutionary state-based tactics generator (ESTG), which uses an evolutionary algorithm to generate tactics automatically. Experimental results show that ESTG improves dynamic scripting's performance in a real-time strategy game. We conclude that high-quality domain knowledge can be automatically generated for strong adaptive game AI opponents.
Drabble, Brian, Chaudron, Laurent, Tessier, Catherine, Abu-Hakima, Sue, Willmott, Steven, Austin, Jim, Faltings, Boi, Freuder, Eugene C., Friedrich, Gerhard, Freitas, Alex A., Cortes, U., Sanchez-Marre, M., Aha, David W., Becerra-Fernandez, Irma, Munoz-Avila, Hector, Ghose, Aditya, Menzies, Tim, Satoh, Ken, Califf, Mary Elaine, Cox, Michael, Sen, Sandip, Brezillon, Patrick, Pomerol, Jean-Charles, Turner, Roy, Turner, Elise
The AAAI-99 Workshop Program (a part of the sixteenth national conference on artificial intelligence) was held in Orlando, Florida. Each workshop was limited to approximately 25 to 50 participants. Participation was by invitation from the workshop organizers. The workshops were Agent-Based Systems in the Business Context, Agents' Conflicts, Artificial Intelligence for Distributed Information Networking, Artificial Intelligence for Electronic Commerce, Computation with Neural Systems Workshop, Configuration, Data Mining with Evolutionary Algorithms: Research Directions (Jointly sponsored by GECCO-99), Environmental Decision Support Systems and Artificial Intelligence, Exploring Synergies of Knowledge Management and Case-Based Reasoning, Intelligent Information Systems, Intelligent Software Engineering, Machine Learning for Information Extraction, Mixed-Initiative Intelligence, Negotiation: Settling Conflicts and Identifying Opportunities, Ontology Management, and Reasoning in Context for AI Applications.
The Fifteenth National Conference on Artificial Intelligence (AAAI-98) was held in Madison, Wisconsin, on 26-30 July. The following four workshops were held in conjunction with the conference: (1) Case-Based Reasoning Integrations, (2) Learning for Text Categorization, (3) Predicting the Future: AI Approaches to Time-Series Problems, and (4) Software Tools for Developing Agents.
Aha, David W.
The 1994 Workshop on Case-Based Reasoning (CBR) focused on the evaluation of CBR theories, models, systems, and system components. The CBR community addressed the evaluation of theories and implemented systems, with the consensus that a balance between novel innovations and evaluations could maximize progress.
The Association for the Advancement of Artificial Intelligence (AAAI) held its 1995 Fall Symposia Series on 10 to 12 November in Cambridge, Massachusetts. This article contains summaries of the eight symposia that were conducted: (1) Active Learning; (2) Adaptation of Knowledge for Reuse; (3) AI Applications in Knowledge Navigation and Retrieval; (4) Computational Models for Integrating Language and Vision; (5) Embodied Language and Action Symposium; (6) Formalizing Context; (7) Genetic Programming; and (8) Rational Agency: Concepts, Theories, Models, and Applications.