Forum for Artificial Intelligence
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The Forum for Artificial Intelligence meets every other week (or so) to discuss scientific, philosophical, and cultural issues in artificial intelligence. Both technical research topics and broader inter-disciplinary aspects of AI are covered, and all are welcome to attend! If you would like to be added to the FAI mailing list, or have any questions or comments, please send email to Igor Karpov or Joseph Reisinger . The current schedule is also available as a Google Calendar or alternatively in iCal format. |
[ Upcoming talks ]
| Friday, December 4 11:00AM ACES 2.302 |
Robin R. Murphy Texas A&M |
Remote Presence: Autonomy Can Be Shared (or Blamed) |
| Friday, January 29 11:00AM TBA |
Esra Erdem Sabanci University |
TBA |
| Friday, March 26 11:00AM ACES 2.402 |
James Lester North Carolina State University |
Narrative-Centered Learning Environments |
Friday, December 4, 11:00AM
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Remote Presence: Autonomy Can Be Shared (or Blamed)Robin R. Murphy [homepage]
[Sign-up schedule for individual meetings]This talk describes a spectrum of three models of teleoperation for remote presence applications, such as emergency response, law enforcement, and military operations in urban terrain, where humans use a robot to obtain real-time perception at a distance. These enterprises are treated as joint cognitive systems and examined in terms of roles, information flow, and team processes. The state of the practice, where the robot has no autonomy, is captured by the Remote Tool Model. The Taskable Agent Model, where the robot has full autonomy and human involvement is negligible represents the other extreme of the spectrum but is not a desirable goal for remote presence applications. A third novel model occupying the space between the two extremes is posited, the Shared Roles Model, which incorporates semi-autonomy and increased communications connectivity. Shared roles provide a naturalistic, explicit representation of the requisite responsibilities and whether the division of functions between the robot and human conserves those responsibilities. The talk discusses whether advances in technology will obviate the Shared Roles Model, what the model implies about the human-robot ratio and whether the ratio can be reduced by merging roles, and identifies open research issues in team processes.About the speaker:Robin Roberson Murphy is the Raytheon Professor of Computer Science and Engineering at Texas A&M. She received a B.M.E. in mechanical engineering, a M.S. and Ph.D in computer science in 1980, 1989, and 1992, respectively, from Georgia Tech, where she was a Rockwell International Doctoral Fellow. Her research interests are artificial intelligence, human-robot interaction, and heterogeneous teams of robots. In 2008, she was awarded the Al Aube Outstanding Contributor award by the AUVSI Foundation for her insertion of ground, air, and sea robots for urban search and rescue (US&R) at the 9/11 World Trade Center disaster, Hurricanes Katrina and Charley, and the Crandall Canyon Utah mine collapse. She is a Distinguished Speaker for the IEEE Robotics and Automation Society, and has served on numerous boards, including the Defense Science Board, USAF SAB, NSF CISE Advisory Council, and DARPA ISAT. |
Friday, January 29, 11:00AM
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TBAEsra Erdem [homepage]
TBA |
Friday, March 26, 11:00AM
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Narrative-Centered Learning EnvironmentsJames Lester [homepage]
The long-term goal of the intelligent tutoring systems community is to create adaptive learning technologies that bring about fundamental improvements in education. For the past several years we have been investigating a family of intelligent tutoring systems that have a dual focus on learning effectiveness and student engagement: narrative-centered learning environments. Narrative-centered learning environments marry the inferential capabilities of intelligent tutoring systems with the rich gameplay supported by commercial game engines. In this talk we will introduce the principles motivating the design of narrative-centered learning environments, describe their roots in interactive narrative, explore the role of computational models of affect recognition and affect expression in their interactions, and discuss their cognitive and affective impact on students through empirical studies conducted in public school systems. The discussion will be illustrated with two narrative-centered learning environments, Crystal Island (elementary science education, middle school science education), and the Narrative Theatre (middle school writing).
About the speaker:Dr. James Lester is Professor of Computer Science at North Carolina State University. He received his Ph.D. in Computer Sciences from the University of Texas at Austin in 1994. He has served as Program Chair for the ACM International Conference on Intelligent User Interfaces (2001), Program Chair for the International Conference on Intelligent Tutoring Systems (2004), Conference Co-Chair for the International Conference on Intelligent Virtual Agents (2008), and on the editorial board of Autonomous Agents and Multi-Agent Systems (1997-2007). His research focuses on intelligent tutoring systems, computational linguistics, and intelligent user interfaces. It has been recognized with a CAREER Award by the National Science Foundation and several Best Paper Awards. His current interests include intelligent game-based learning environments, affective computing, creativity-enhancing technologies, computational models of narrative, and tutorial dialogue. He is Editor-in-Chief of the International Journal of Artificial Intelligence in Education. |
[ Past talks]
Tuesday, August 4, 4:00PM
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Building a Comprehensive Model for the Development and Function of the Visual CortexJames A. Bednar [homepage]
Vision is a standard system for studying cortical sensory processing. Previous computational models of adult primary visual cortex (V1) have been able to account for many of the measured properties of V1 neurons, but not how or why these particular properties arise. Previous developmental models have been able to reproduce the overall organization of specific feature maps in V1, such as orientation maps, but the neurons in the simulated maps behave quite unlike real V1 neurons, and in many cases are too abstract even to be testable on actual visual stimuli. I believe that the complex adult circuitry only makes sense when considering the developmental process that created it, and conversely, that the developmental process only makes sense if leading to a system that can perform behaviorally relevant visual tasks. Accordingly, in this talk I outline a long-term project to build the first model to explain both the development and the function of V1. To do this, researchers in my group are building the first developmental models with wiring consistent with V1, the first to have realistic behavior with respect to visual contrast, the first to include all of the various visual feature dimensions, and the first to include all of the major sources of connectivity that modulate V1 neuron responses. The goal is to have a comprehensive explanation for why V1 is wired as it is in the adult, and how that circuitry leads to the observed behavior of the neurons during visual tasks. This approach leads to experimentally testable predictions at each stage, and can also be applied to understanding other sensory cortices, such as somatosensory and auditory cortex. About the speaker:Jim Bednar leads the Computational Systems Neuroscience research group at the University of Edinburgh, and is the deputy director of the Edinburgh Doctoral Training Centre in Neuroinformatics and Computational Neuroscience. His 2002 Ph.D. in Computer Science is from the University of Texas at Austin, and he also has degrees in Philosophy and Electrical Engineering. His research focuses on computational modeling of the development and function of mammalian visual systems. He is a co-author of the monograph "Computational Maps in the Visual Cortex" (Springer, 2005), and is the lead author of the Topographica cortical modeling software package (see topographica.org). He is also a member of the Board of Directors for the annual international Computational Neuroscience Meeting. |
Tuesday, September 1, 11:00AM
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Doing More with Less: Mutual Interdependence AnalysisJustinian Rosca [homepage]
The mean of a data set is one trivial representation of data from a class. Recently, mutual interdependence analysis (MIA) has been successfully used to extract more involved representations, or "mutual features", accounting for samples in the class. For example a mutual feature is a speaker signature under varying channel conditions or a face signature under varying illumination conditions. A mutual representation is a linear regression that is equally correlated with all samples of the input class. We present the MIA optimization criterion from the perspectives of regression, canonical correlation analysis and Bayesian estimation. This allows us to state and solve the MIA criterion concisely, to contrast the MIA solution to the sample mean, and to infer other properties of its closed form, unique solution under various statistical assumptions. This work has been done in collaboration with Heiko Claussen (Siemens Corporate Research, Princeton, NJ) and Robert Damper (Univ. Southampton, UK). About the speaker:Justinian Rosca received his Ph.D. in Computer Science from the University of Rochester, NY. He is Program Manager in Audio, Signal Processing and Wireless Communications at Siemens Corporate Research in Princeton, NJ, and also Affiliate Professor, Department of Electrical Engineering of University of Washington, Seattle. He conducts research in signal processing and radio management, with an emphasis on topics involving acquisition, management and processing of data with uncertainties, statistical audio processing, blind signal separation, and probabilistic inference. Dr. Rosca has more that two dozen US and international patents awarded. He co-authored two books in mathematics and signal processing, most recently he co-edited the Proceedings of the 6th International Conference on Independent Component Analysis and Blind Signal Separation. He is presently on the editorial board of the Journal of Signal Processing Systems from Springer. Within Siemens, he has lead the Audio Signal Processing program in the development of state-of-the-art algorithms and implementations in microphone array signal processing, blind signal separation, sound analysis and identification with applications in hearing aids. |
Friday, September 11, 11:00AM
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Teammates in Ad Hoc Teams or What I did on my sabbaticalPeter Stone [homepage]
Teams of agents, defined as agents operating in the same environment with identical utility functions, are typically developed in a planned, coordinated fashion. However, such coordinated development is not always possible. Rather, as deployed agents become more common in robotics, e-commerce, and other settings, there are increasing opportunities for previously unacquainted agents to cooperate in ad hoc team settings. In such scenarios, it is useful for individual agents to be able to collaborate with a wide variety of possible teammates under the philosophy that not all agents are fully rational. This talk considers an agent that is to interact repeatedly with a teammate that will adapt to this interaction in a particular suboptimal, but natural way. We formalize this "ad hoc team" framework in two ways. First, in a fully cooperative normal form game-theoretic setting, we provide and analyze a fully-implemented algorithm for finding optimal action sequences, prove some theoretical results pertaining to the lengths of these action sequences, and provide empirical results pertaining to the prevalence of our problem of interest in random interaction settings. Second, we consider a cooperative k-armed bandit in which cooperating agents have access to different actions (arms). In this setting we prove some theoretical results pertaining to which actions are potentially optimal, provide a fully-implemented algorithm for finding such optimal actions, and provide empirical results. About the speaker:Dr. Peter Stone is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, Fulbright Scholar, and Associate Professor in the Department of Computer Sciences at the University of Texas at Austin. He received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. Peter's research interests include machine learning, multiagent systems, robotics, and e-commerce. In 2003, he won a CAREER award from the National Science Foundation for his research on learning agents in dynamic, collaborative, and adversarial multiagent environments. In 2004, he was named an ONR Young Investigator for his research on machine learning on physical robots. In 2007, he was awarded the prestigious IJCAI 2007 Computers and Thought award, given once every two years to the top AI researcher under the age of 35. |
Friday, October 9, 11:00AM
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Statistics-based real-time sports simulationUbbo Visser [homepage]
Autonomous agents in real-time and dynamic adversarial environments offer numerous research challenges. Perception, localization, decision-making, communication, and locomotion are good examples. The novel modern sports simulator we will discuss integrates results from ten years of research in the area of autonomous soccer playing robots (both softbots and physical robots) with RoboCup as a testbed. We will explore the problem of enabling autonomous agents in finding the right passing point or in making a complex decision within a soccer game while dealing with time constrains, hostile opponents, and dynamic environments. We propose a framework for spatio-temporal real-time analysis of dynamic scenes. We introduce a knowledge processing pipeline ranging from relevance-driven compilation of a qualitative scene description to a knowledge-based detection of complex event and action sequences, conceived as a spatio-temporal pattern matching problem. We present experimental results from the RoboCup 3D soccer simulation that substantiate the online applicability of our approach under tournament conditions. We then take a closer look at new generation sports simulation online games where the results of this research have been integrated. 175,000 users currently operate the 'Official Bundesliga Manager' created by the University of Bremen's spin-off company aitainment GmbH and which was adopted by the German Bundesliga (one of the most prestigious soccer leagues in the world). The 'Official Bundesliga Manager' is a complex real-time soccer simulator that is based on user-models from actual data (e.g. passing performance, scoring accuracy) of real soccer players from the German Bundesliga. The underlying hierarchical three-tier multiagent system consists of autonomous BDI agents that allows dynamic group structures (e.g. an emergent situation for a wing attack). The online game runs seamlessly in a web browser with a new and state-of-the-art 3D visualization engine. Latest developments include research results from a motion capturing lab and face generators to enhance the believability of the players and the users' visualization experience. About the speaker:Dr. Ubbo Visser is currently a Visiting Associate Professor in the Department of Computer Science at the University of Miami. He received his Habilitation in Computer Science (qualification for Full Professor) from the University of Bremen in 2003, his PhD in Geoinformatics from University of Muenster in 1995, and his MSc in Landscape-ecology from University of Muenster in 1988. His research specialization is in artificial intelligence, more specifically on knowledge representation and reasoning. He is interested in the combination of symbolic and sub-symbolic technologies in the domain areas of "Semantic Web" and "Multiagent Systems (RoboCup, Games)". His focus in the Semantic Web area lies in the development of methods that combine terminological logics and spatio-temporal representation and reasoning techniques. The focus in the Multiagent Systems area lies in the development of techniques for agents that act in highly dynamic and real-time environments. He won several awards for research and development of innovative AI software (e.g. Best AI Award from the Society for Informatics (GI) in Germany) and is a co-founder of innovative software companies both in Europe and in the United States. |
Thursday, October 22, 3:30PM
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A Visitor's Companion Robot: Localization, Navigation, and Symbiotic Human-Robot InteractionManuela Veloso [homepage]
We have been developing an indoor robot, CoBot, as a visitor's companion robot. CoBot moves in buildings for which it knows the map, but that offer dynamic challenges, such as people moving and obstacles without fixed placements. I will present the wifi-based localization and navigation algorithms, illustrating them with examples of multiple hours-long autonomous runs of the robot. I will conclude with our symbiotic interaction algorithm in which the robot and the human have complementary limitations and expertise. This work is joint with my students Joydeep Biswas, Stephanie Rosenthal, and Nick Armstrong-Crews. The robot was designed and constructed by Michael Licitra, as an omnidirectional four-wheeled robot, inspired by his own previous platform of our small-size soccer robots. About the speaker:Manuela M. Veloso is the Herbert A. Simon Professor of Computer Science at Carnegie Mellon University. She researches in artificial intelligence and robotics, in the areas of planning and learning for single and multirobot teams in uncertain, dynamic, and adversarial environments. Veloso is a Fellow of the American Association of Artificial Intelligence, and the President of the RoboCup Federation. She was recently awarded the 2009 ACM/SIGART Autonomous Agents Research Award. Veloso is the author of one book on "Planning by Analogical Reasoning" and editor of several other books. She is also an author in over 200 journal articles and conference papers. |
Friday, November 6, 11:00AM
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Comparing versions of electronic or paper-based documents with Magic LensCynthia Thompson [homepage]
PricewaterhouseCoopers (PwC) professionals review, compare, and opine on thousands of documents which originate in multiple file formats, with many available only on paper. Magic Lens is a technology enabled document review tool that automatically highlights differences in text when comparing similar documents, reducing document review time by up to 80%. This unique application compares more than just Word documents; it also handles Adobe PDF, scanned paper documents, and even compares documents of different formats. PwC professionals use Magic Lens to compare and review revenue contracts, 10-Ks, invoices, financial agreements and other critical documents with increased accuracy and efficiency. I will discuss the novel and efficient algorithm for text comparison that we created for Magic Lens, which is capable of finding matching text even when it moves to a different location in a document. We also designed and implemented a novel document comparison user interface. Magic Lens has been used by over 5000 PwC partners and staff. I will also provide a brief overview of other current & past CAR projects. The PricewaterhouseCoopers Center for Advanced Research (CAR) conducts PwC-sponsored research and development on business problems that have no known solution in the marketplace. Since innovation often results from combining widely-varied insights, an important part of CAR's strategy is to hire researchers with backgrounds in a variety of fields, including computer science, mathematics, economics, statistics, mechanical engineering, and software development, and further cross pollinate their knowledge with professionals and subject matter specialists from various PwC lines of service. The diversity in this collaboration results in fresh perspectives on real business problems. Located in the heart of Silicon Valley in PwC's San Jose office, CAR began operation in 2003 under the leadership of PricewaterhouseCoopers partner and technology entrepreneur, Sheldon Laube. We are now accepting applications for spring and summer internships, and Dr. Thompson will be available to meet with interested students. About the speaker:Dr. Thompson (Cindi) is a Senior Research Manager at PwC. She led the Magic Lens project for two and a half years and is now leading a new project investigating the impact of interruptions on productivity and quality. Prior to leading Magic Lens, she contributed to the Connection Machine project, an adaptive expertise locator system that, and which was also deployed to the US Firm. Prior to joining PwC, Dr. Thompson was an Assistant Professor in the School of Computing at the University of Utah. Her research focused on the development and application of techniques from machine learning to natural language understanding, scientific time series, and recommendation systems. Cindi's teaching included courses on Machine Learning, Artificial Intelligence, and Discrete Mathematics. Her publications include book chapters in Learning Language in Logic and articles for the Journal of Artificial Intelligence Research, and many conference paper publications. Cindi also served as Consulting Researcher at Stanford University's Institute for the Study of Learning and Expertise, and during the 2002-03 academic year served as Visiting Assistant Professor in the Computer Science Department at Stanford. She was a Postdoctoral Research Fellow at Stanford University's Center for the Study of Language and Information. Her postdoctoral research subject was spoken dialogue systems that change their interaction behavior based on past interactions with users. Cindi received her Ph.D. and M.A. in Computer Science from the University of Texas at Austin under Professor Ray Mooney, and her B.S. in Computer Science from North Carolina State University. |
Friday, November 20, 11:00AM
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Embracing Language Diversity: Unsupervised Multilingual Learning?Regina Barzilay [homepage]
For centuries, the deep connection between human languages has fascinated scholars, and driven many important discoveries in linguistics and anthropology. In this talk, I will show that this connection can empower unsupervised methods for language analysis. The key insight is that joint learning from several languages reduces uncertainty about the linguistic structure of each individual language. I will present multilingual generative unsupervised models for morphological segmentation, part-of-speech tagging, and parsing. In all of these instances we model the multilingual data as arising through a combination of language-independent and language-specific probabilistic processes. This feature allows the model to identify and learn from recurring cross-lingual patterns to improve prediction accuracy in each language. I will also discuss ongoing work on unsupervised decoding of ancient Ugaritic tablets using data from related Semitic languages. This is joint work with Benjamin Snyder, Tahira Naseem and Jacob Eisenstein. |