Ramachandran, Sowmya
Reports of the AAAI 2011 Conference Workshops
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.
Reports of the AAAI 2011 Conference Workshops
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' (University College Cork) | Sullivan, Barry (Cognitive Systems Research Insitute) | Pastra, Katerina (McGill University) | Precup, Doina (Stottler Henke Associates, Inc.) | Ramachandran, Sowmya (University of Dundee) | Reed, Chris (Istanbul Technical University) | Sariel-Talay, Sanem (Carnegie Mellon University) | Selker, Ted (Infosys Technologies Ltd.) | Shastri, Lokendra (Carnegie Mellon University) | Smith, Stephen F. (University of Michigan at Ann Arbor) | Singh, Satinder (University of Wisconsin, Madison) | Srivastava, Siddharth (University of Central Florida) | Sukthankar, Gita (Naval Research Laboratory) | Uthus, David C. (University of Technology, Sydney) | Williams, Mary-Anne
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.
Untangling Topic Threads in Chat-Based Communication: A Case Study
Ramachandran, Sowmya (Stottler Henke Associates Inc.) | Jensen, Randy (Stottler Henke Associates Inc.) | Bascara, Oscar (Stottler Henke Associates Inc.) | Carpenter, Tamitha (Stottler Henke Associates Inc.) | Denning, Todd (US Air Force) | Sucillon, Lt. Shaun (US Air Force Research Laboratory)
Analyzing chat traffic has important applications for both the military and the civilian world. This paper presents a case study of a real-world application of chat analysis in support of team training exercise in the military. It compares the results of an unsupervised learning approach with those of a supervised classification approach. The paper also discusses some of the specific challenges presented by this domain.
Mining Chat Conversations: The Next Frontier
Ramachandran, Sowmya (Stottler Henke Associates Inc) | Jensen, Randy (Stottler Henke Associates, Inc) | Bascara, Oscar (Stottler Henke Associates, Inc) | Carpenter, Tamitha (Stottler Henke Associates Inc) | Denning, Todd ( AFRL/RHA ) | Sucillon, Shaun (AFRL)
Analyzing chat traffic has important applications for both the military and the civilian world. This poster will report on an effort to automatically separate chat messages into topic threads.
Information Measure Based Skeletonisation
Ramachandran, Sowmya, Pratt, Lorien Y.
Automatic determination of proper neural network topology by trimming oversized networks is an important area of study, which has previously been addressed using a variety of techniques. In this paper, we present Information Measure Based Skeletonisation (IMBS), a new approach to this problem where superfluous hidden units are removed based on their information measure (1M). This measure, borrowed from decision tree induction techniques,reflects the degree to which the hyperplane formed by a hidden unit discriminates between training data classes. We show the results of applying IMBS to three classification tasks and demonstrate that it removes a substantial number of hidden units without significantly affecting network performance.
Information Measure Based Skeletonisation
Ramachandran, Sowmya, Pratt, Lorien Y.
Automatic determination of proper neural network topology by trimming oversized networks is an important area of study, which has previously been addressed using a variety of techniques. In this paper, we present Information Measure Based Skeletonisation (IMBS), a new approach to this problem where superfluous hidden units are removed based on their information measure (1M). This measure, borrowed from decision tree induction techniques, reflects the degree to which the hyperplane formed by a hidden unit discriminates between training data classes. We show the results of applying IMBS to three classification tasks and demonstrate that it removes a substantial number of hidden units without significantly affecting network performance.