The Tutorial Forum of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08) will be held July 13-14, 2008 in Chicago. The Tutorial Forum provides an opportunity for junior and senior researchers to spend two days each year freely exploring exciting advances in disciplines outside their normal focus. We believe this type of forum is essential for the cross fertilization, cohesiveness, and vitality of the AI field. We all have a lot to learn from each other; the Tutorial Forum promotes the continuing education of each member of the AAAI. Registration for tutorials will be available in March 2008.
What Is the Tutorial Forum?
The Tutorial Forum provides an opportunity for junior and senior researchers to spend two days each year freely exploring exciting advances in disciplines outside their normal focus. We believe this type of forum is essential for the cross fertilization, cohesiveness, and vitality of the AI field. We all have a lot to learn from each other; the Tutorial Forum promotes the continuing education of each member of the AAAI.
2008 Tutorials
- SA1: Computational Workflows for Large-Scale AI Research
Yolanda Gil (ISI/University of Southern California) - SA2: Decision-Theoretic Planning and Learning in Relational Domains
Prasad Tadepalli (Oregon State University), Alan Fern (Oregon State University), and Kristian Kersting (Massachusetts Institute of Technology) - SA3: Graphical Models for Multiagent Decision-Making
Avi Pfeffer (Harvard University) and Ya’akov (Kobi) Gal (Harvard University) - SA4: Machine Learning for Biomedical Applications
David Page (University of Wisconsin Medical School) - SP1: Computational Mechanism Design with Applications to E-Commerce and Planning
David Parkes (Harvard University) - SP2: Human-Robot Interaction
Holly A. Yanco (University of Massachusetts Lowell) - SP3: Satisfied by Message Passing: Probabilistic Techniques for Combinatorial Problems
Lukas Kroc (Cornell University), Ashish Sabharwal (Cornell University), and Bart Selman (Cornell University) - SP4: Social Network Mining: A Tutorial on Inference and Learning with Social Network Data
Jennifer Neville (Purdue University) and Foster Provost (New York University) - MA1: Ambient Intelligence: Applications in Society and Opportunities for AI
Juan Carlos Augusto (University of Ulster at Jordanstown), Diane Cook (Washington State University), and Hans W. Guesgen (Massey University) - MA2: General Game Playing
Michael Thielscher (Dresden University of Technology) - MA3: Path Planning
Michael Buro (University of Alberta), Sven Koenig (University of Southern California (USC)), and Nathan Sturtevant (University of Alberta) - MA4: Visual Recognition
Kristen Grauman (University of Texas at Austin) and Bastian Leibe (ETH Zurich) - MP1: External-Memory Graph Search
Stefan Edelkamp (Universität Dortmund), Eric Hansen (Mississippi State University), Shahid Jabbar (Universität Dortmund), and Rong Zhou (Palo Alto Research Center) - MP2: Introduction to Sketch Recognition
Tracy Hammond (Texas A&M University), Brandon Paulson (Texas A&M University), Katie Dahmen (Texas A&M University), Brian Eoff (Texas A&M University), and Aaron Wolin (Texas A&M University) - MP3: The Many Faces of Logistic Regression
David D. Lewis
Key
SA = Sunday, July 13, 9:00 am – 1:00 pm
SP = Sunday, July 13, 2:00 pm – 6:00 pm
MA = Monday, July 14, 9:00 am – 1:00 pm
MP = Monday, July 14, 2:00 pm – 6:00 pm
SA1 Computational Workflows for Large-Scale AI Research
Yolanda Gil (ISI/University of Southern California)
The goal of the Computational Workflows for Large-Scale AI Research tutorial is twofold: first, to introduce computational workflows to AI researchers as a powerful paradigm they can use to manage large-scale experimentation, and second, to present interesting research problems for AI that arise in developing workflow systems. The tutorial will begin with an introduction to the stages of design of workflows, and the capabilities of current workflow systems being used in a variety of scientific domains to specify and manage thousands of distributed computations. The introduction will include examples of computational workflows in several science applications that specify the analysis steps to be executed and the data flow among them. It will also describe how to design these steps as encapsulated software components so that the workflow system can automatically manage execution and tailor it to available computing resources. The second part of the tutorial will present recent work on applying computational workflows to artificial intelligence as a scientific domain, in particular for large-scale integrative machine learning and for natural language processing. The last part of the tutorial will introduce a variety of AI techniques that are relevant to current challenges in computational workflows, including dynamic self-configuration, interactive steering, continuous and robust operations, and performance optimization.
Yolanda Gil is the associate division director at USC’s Information Sciences Institute and a research associate professor in computer science. Her interests include intelligent user interfaces and knowledge-rich problem solving. She was an elected councilor of AAAI and was the 2006 conference program cochair. She serves in the NSF CISE Advisory Committee.
SA2 Decision-Theoretic Planning and Learning in Relational Domains
Prasad Tadepalli (Oregon State University), Alan Fern (Oregon State University), and Kristian Kersting (Massachusetts Institute of Technology)
AI has long been interested in planning in deterministic domains with rich relational structure. In the past twenty years, ideas from probability theory and decision theory have made inroads to AI and led to many new developments including Bayesian networks and reinforcement learning. While most of this work is done in the context of propositional representations, there have been some exciting new developments in the last decade in combining the rich relational representations of classical planning with the decision-theoretic notions of reasoning under uncertainty and choosing actions to maximize the expected utility. This tutorial surveys this research area starting from the foundations of Markov decision processes (MDPs), leading to the relational extensions of a variety of classical approaches such as policy iteration and linear programming to solving Markov decision processes. This is a topic of high current interest since it seeks to combine the expressive representations of classical AI with the decision-theoretic emphasis of modern AI. The tutorial is self-contained and assumes only minimal computer science and AI background. The target audiences are graduate students and researchers interested in planning and learning using probabilistic and relational representations.
Prasad Tadepalli earned his Ph.D. in computer science from Rutgers University in 1990 and joined the faculty at Oregon State University. His research interests include reinforcement learning, structured machine learning, computational learning theory, and speedup learning. He serves as an action editor of the journal of Machine Learning.
Alan Fern received his Ph.D. from Purdue University in 2004. He is currently an assistant professor in the School of Electrical Engineering and Computer Science at Oregon State University. His primary research interests include learning and planning in structured domains.
Kristian Kersting received his Ph.D. from the University of Freiburg, Germany in 2006. After a postDoc at MIT in 2007, he joined Fraunhofer IAIS, Germany as a researcher. His main research interest is statistical relational learning. He has received the ECML-06 best student paper award and the ECCAI-06 dissertation award.
SA3 Graphical Models for Multi-Agent Decision-Making
Avi Pfeffer (Harvard University) and Ya’akov (Kobi) Gal (Harvard University)
Recent work at the interface between artificial intelligence and game theory has provided natural, compact representations for describing multi-agent interaction under uncertainty. These representations use graphical networks, and make explicit the dependency relationships that hold between agents’ decisions, utilities and the chance variables in the environment, which may be then exploited for efficient inference. Graphical networks facilitate the decomposition of complex multi-agent decision-making problems into smaller, interacting constituents which can be analyzed independently in a way that preserves the global structure of the problem. Applications using graphical networks for decision-making abound, and include vehicle tracking, military combat simulations and human-computer negotiation.
This half-day tutorial will survey these new formalisms, focusing on their expressiveness as knowledge representation languages, and the challenges they pose to inference algorithms. It will show how to construct networks that adequately capture the relationship between agents’ beliefs about each other’s strategies and the environment. It will present a variety of techniques that exploit the network structure in order to efficiently extract equilibrium strategies for agents that satisfy various conditions. Throughout the tutorial, both theory and algorithms will be exemplified using the IBAL programming language, a probabilistic language that is generally applicable to graphical fomrlaims.
The tutorial presupposes a basic understanding of probability theory, but is self contained in all other aspects. It does not assume knowledge of game theoretic concepts, nor previous exposure to multiagent systems research, or probabilistic reasoning.
Avi Pfeffer is an associate professor of computer science at Harvard University. His research is directed towards achieving rational behavior in intelligent systems, based on the principles of probability theory, decision theory, Bayesian learning and game theory. He received his Ph.D. in 2000 from Stanford University, where his dissertation on probabilistic reasoning received the Arthur Samuel Thesis award. feffer has published technical papers on probabilistic reasoning, strategic reasoning, agent modeling, temporal reasoning, and database systems. He was awarded the NSF Career award in 2001 for work on strategic reasoning, and the Alfred P. Sloan Foundation Research fellowship in 2002.
Ya’akov (Kobi) Gal is a post-doctoral researcher at the MIT Artificial Intelligence lab and at Harvard University’s School of Engineering and Applied Sciences. His research focuses on representations and algorithms for reasoning about agents’ beliefs and decision-making processes. He is a two-time recipient of Harvard University’s Derek Bok award for excellence in teaching, as well as the School of Engineering and Applied Science’s outstanding teacher award. He has published technical papers focusing on human-computer decision-making, and opponent modeling.
SA4 Machine Learning for Biomedical Applications
David Page (University of Wisconsin Medical School)
Biotechnology makes possible the performance of thousands of experiments in the time it used to take to perform just one. The first part of the tutorial covers a variety of machine learning challenges and research opportunities arising from high-throughput biological data. Example technologies include gene expression microarrays, mass spectrometry for proteomics and metabonomics, single-nucleotide polymorphism arrays for genotyping, and robotic high-throughput screening for potential drug compounds. Concurrent with advances in biotechnology, medical practice has been shifting to a “paperless” electronic medical record. This fundamental shift, combined with the availability of high-throughput genotyping already mentioned, raises the prospect of predictive medicine. Because of publicized cases of serious adverse drug reactions, as well as difficulties in drug selection and dosing, demand for predictive medicine is rising dramatically. The remainder of the tutorial discusses the role that machine learning can play in predictive medicine. Throughout the tutorial, we will discuss case studies and lessons they provide for machine learning researchers.
David Page received his Ph.D. in computer science from the University of Illinois at Urbana-Champaign in 1993. He then joined Stephen Muggleton’s group at the Oxford University Computing Laboratory, where he became involved in biomedical applications of machine learning. Page now serves on the faculty at the University of Wisconsin-Madison, in the School of Medicine and Public Health. He is also a member of the Computer Science Department, the Genome Center of Wisconsin and the UW-Madison Comprehensive Cancer Center.
SP1 Computational Mechanism Design with Applications to E-Commerce and Planning
David Parkes (Harvard University)
Mechanism design (MD) is the subfield of microeconomics that seeks to understand how to implement desirable system-wide solutions in environments with multiple self-interested agents, each with private information about their preferences and capabilities. Computational mechanism design seeks to bridge between the ‘in principle’ theory of mechanism design to the practical deployment of mechanisms for resource allocation, electronic commerce, and within AI applications including to distributed planning and sensor fusion. We begin with an introduction to mechanism-design theory and its central concepts and results: the revelation principle, incentive-compatibility, the Vickrey-Clarke-Groves mechanism, optimal auctions, monotonicity and settings with interdependent values and externalities. We then consider how to combine these considerations with computational insights, adopting applications from sponsored search, combinatorial auctions, sensor fusion, and expressive commerce. Finally, we explore an interesting frontier of computational MD: extending mechanisms to provide coordinated planning in dynamic, multi-agent environments. Attendees need not have any familiarity with mechanism design. The audience will gain the ability to develop mechanisms with applications to E-Commerce and multiagent systems, and the background to do research in computational mechanism design. The tutorial assumes knowledge of elementary economic theory, including utility theory and game theory, and some familiarity with optimization and computational complexity.
David Parkes is an associate professor of computer science at Harvard University. A recipient of the NSF Career award and an Alfred P. Sloan Fellow, Parkes has published extensively on topics related to e-markets, CMD and multiagent systems. He serves on the editorial board of JAIR, as an editor of Games and Economic Behavior, and as program chair of ACMEC’07 and AAMAS’08.
SP2 Human-Robot Interaction
Holly A. Yanco (University of Massachusetts Lowell)
Human-robot interaction (HRI) is an emerging field; the first international conference on this topic was held in 2006. This relatively recent emergence is the result of the development of more stable robot platforms that can run for longer periods of time in more complex situations. When robots could only run for short periods of time, researchers spent little effort thinking about how people would interact with the robots; many interfaces were only usable by the engineers who had built the robots. With robots now being sold in stores as well as being deployed widely in the military, we must consider how people will interact with robots in a number of roles. The tutorial will cover many areas of human-robot interaction, including how robot morphologies and behaviors influence a person’s expectations for interaction, the types of interaction modalities that can be used, how to design interfaces to enhance situation awareness, how to design for differing interaction distances, how different robot autonomy levels affect human-robot interaction, and methods and metrics for evaluating systems. The tutorial will draw on examples from many application domains, including assistive technology (AT) and urban search and rescue (USAR). No prior knowledge of robotics is required.
Holly Yanco is an associate professor in the Computer Science Department at the University of Massachusetts Lowell, where she heads the robotics laboratory. Her research addresses many aspects of human-robot interaction, including sensor processing and fusion, interface design, sliding scale autonomy, metrics for assessing HRI, and testing methodologies. She received a Career Award from NSF in 2006. She has a Ph.D. and MS from MIT and a BA from Wellesley College, all in computer science. Yanco is a member of AAAI’s Executive Council. She has received teaching awards from MIT and UMass Lowell.
SP3 Satisfied by Message Passing: Probabilistic Techniques for Combinatorial Problems
Lukas Kroc (Cornell University), Ashish Sabharwal (Cornell University), and Bart Selman (Cornell University)
Pushing the boundary of practical solvability of certain combinatorial problems, such as Boolean satisfiability (SAT) or graph coloring, has far reaching practical consequences. This tutorial will introduce an exciting and relatively new algorithm for such problems called survey propagation. Stemming from physics research, survey propagation is so far the only known approach successful at solving hard random SAT problems with one million variables in a few minutes on a desktop computer, performance which is clearly beyond the reach of mainstream SAT solvers. The key of survey propagation’s success lies in its ability to quickly compute an approximate solution of a related problem using a message passing process, and use that to solve the original task.
The tutorial, after covering the necessary background in message passing algorithms, will discuss various research aspects of SP. How can one understand survey propagation using the “usual language” of computer science? How does it relate to the known concept of belief propagation? Is it possible to extend the success of survey propagation to problems beyond random SAT? We will present the state of the art in understanding of survey propagation, as well as provide a step-by-step introduction to the subject.
Prerequisite knowledge: basic understanding of Boolean formulas, probabilities, and combinatorial arguments. No prior knowledge of message passing algorithms required.
Lukas Kroc is a Ph.D. student in computer science at Cornell University, under the supervision of Professor Bart Selman. His research interests lie in the field of artificial intelligence, especially using techniques from probabilistic reasoning to solve combinatorial problems, such as SAT or model counting.
Ashish Sabharwal is a postdoctoral associate in computer science at Cornell University. He received his Ph.D. from the University of Washington in 2005. He has (co-)authored over 30 publications and surveys, including two best paper awards and four nominations. His research interests include artificial intelligence, automated reasoning (SAT, CSP, QBF), planning, probabilistic inference, complexity, and algorithms.
Bart Selman is a professor of computer science at Cornell University. He was previously at AT&T Bell Laboratories. His research interests include reasoning, planning, knowledge representation, and connections to statistical physics. He has (co-)authored over 100 publications, including six best paper awards. He has received the Cornell Miles Excellence in Teaching award, the Cornell Outstanding Educator award, an NSF Career award, and a Sloan Research fellowship. He is a Fellow of AAAI and AAAS.
SP4 Social Network Mining
Jennifer Neville (Purdue University) and Foster Provost (New York University)
Recently there has been a surge of interest in methods for analyzing complex social networks-from communication networks, to friendship networks, to professional and organizational networks. The dependencies among linked entities in the networks present an opportunity to improve inference about properties of individuals-as birds of a feather do indeed flock together. For example, when deciding how to market a product to people in MySpace or Facebook, it may be helpful to consider whether a person’s friends are likely to purchase the product.
This tutorial will explore the unique opportunities and challenges for modeling social network data. We will begin with a description of the problem setting, including examples of various applications of social network mining (for example, marketing, fraud detection). We will then present a number of characteristics of social network data that differentiate it from traditional inference and learning settings, and outline the resulting opportunities for significantly improved inference and learning. We will discuss specific techniques for capitalizing on each of the opportunities in statistical models, and outline both methodological issues and potential modeling pathologies that are unique to network data. We will give links to the recent literature to guide study, and present results demonstrating the effectiveness of the techniques.
Prerequisites: The tutorial assumes a basic knowledge of AI-style inference and machine learning, equivalent to an introductory graduate or advanced undergraduate class.
Jennifer Neville is an assistant professor at Purdue University. She received her Ph.D. from the University of Massachusetts Amherst in 2006. She was awarded a DARPA IPTO young investigator award and is a member of the DARPA Computer Science Study Group. Her research focuses on data mining techniques for relational and network domains.
Foster Provost is an associate professor and NEC faculty fellow at New York University’s Stern School. He is editor-in-chief of the Machine Learning journal, a founding board member of the International Machine Learning Society, and was program chair of the ACM SIGKDD Conference in 2001. He has received faculty awards from IBM and a president’s award from NYNEX Science and Technology. His recent research has focused on inference and learning with network data and utility-based data mining.
MA1 Ambient Intelligence
Juan Carlos Augusto (University of Ulster at Jordanstown), Diane Cook (Washington State University), and Hans W. Guesgen (Massey University)
This tutorial will introduce the audience to a new and emerging field of AI, ambient intelligence, which is rising as one of the AI-based paradigms with the highest potential to make an impact in daily human life during the near future. The broad idea is to enrich a space (room, house, building, bus station, a critical area in a hospital, and so on) with sensors so that the people using that space can benefit somehow from a more flexible and intelligent environment. Expected benefits can be increasing safety and comfort, encouraging a healthier life style, and others according to the domain of application.
Since ambient intelligence is a multidisciplinary area where software is related to sensors integrating many areas of AI, it is appropriate to a general audience. Participants are expected to have a basic understanding of AI methods and their applications. The tutorial introduces them to applications of ambient intelligence, which are starting to have a practical impact in the real world today. It will offer the opportunity for AI researchers and practitioners of different areas of AI to explore the potential of ambient-intelligence systems for the AI community and for society in general.
Juan C. Augusto is a lecturer at the University of Ulster at Jordanstown, UK. His area of expertise within AI includes nonmonotonic reasoning, belief revision, defeasible reasoning, logic and argumentation systems, and temporal information representation. Recently he has turned his attention to ambient intelligence in general with emphasis on smart homes.
Diane Cook is a Huie-Rogers chair professor at Washington State University. Her research interests include machine learning, data mining, robotics, smart environments, and parallel algorithms for AI. She is a director of the AI Laboratory and serves as editor-in-chief of IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics.
Hans W. Guesgen holds a chair in computer science at Massey University, New Zealand. He has taught courses at all levels of the computer science curriculum for more than 14 years and has published extensively in his area of research, which in particular includes spatiotemporal aspects of ambient intelligence.
MA2 General Game Playing
Michael Thielscher (Dresden University of Technology)
General game playing is concerned with the development of systems that can play well arbitrary games solely by being given the rules of a game. This raises a number of issues different from traditional research in game playing, where it is assumed that the rules of a game are known to the programmer. Systems able to play hitherto unknown games cannot be given game-specific knowledge. They rather need to be endowed with high-level cognitive abilities such as general strategic thinking, abstract reasoning, and learning. General Game Playing has recently been proposed as one of the grand contemporary AI challenges, encompassing a variety of research areas such as knowledge representation and reasoning, heuristic search and planning, game playing, and learning. General Game Playing has a number of immediate potential applications, including game playing programs that can be adapted by their users and advice giving systems for negotiations, marketing strategies, pricing, and so on.
The tutorial is directed at every AI researcher who wants to gain an insight into general game playing as a method to formalize and solve general decision-making problems in competitive environments. The only required background is some basic knowledge of standard first-order logic.
Michael Thielscher is a professor at Dresden University and head of the Computational Logic Group. His research is mainly in knowledge representation, cognitive robotics, commonsense reasoning, game playing, and constraint logic programming. He coauthored the program FLUXPLAYER, which in 2006 was crowned the world champion at the Second General Game Playing Competition in Boston.
MA3 Path Planning
Michael Buro (University of Alberta), Sven Koenig (University of Southern California (USC)), and Nathan Sturtevant (University of Alberta)
Path planning is fundamental in a variety of research areas from robotics to computer games. Research on path planning has recently made progress in several directions that are not contained in the standard textbooks on artificial intelligence. Specific topics covered will include the use of abstraction for path planning, any-angle path planning, path planning with incremental search, and path planning in polygonal worlds. The tutorial will teach these recent ideas and insights in a self-contained and integrated way, requiring only prior knowledge equivalent to undergraduate classes on algorithms, data structures and artificial intelligence. It will contain both material that has been published in robotics but not artificial intelligence, as well as material that has been published in artificial intelligence but not robotics. It is therefore of interest to graduate students, researchers and practitioners in a variety of areas of artificial intelligence and robotics, including esoteric areas such as car navigation systems and real-time games.
Michael Buro is an associate professor in computing science at the University of Alberta in Edmonton, Canada. He is the author of Logistello, an Othello program that defeated the world-champion 6-0 in 1997. Buro’s current research interests include real-time planning, automated abstraction, and reasoning under uncertainty, which he studies in the real-time strategy (RTS) and card game domains.
Sven Koenig is an associate professor in computer science at the University of Southern California. He received his Ph.D. from Carnegie Mellon University in 1997. Koenig’s research on incremental search has been adapted by others for a variety of applications, from Mars rovers to cars that drive autonomously.
Nathan Sturtevant is a post-doctoral researcher in computer science at the University of Alberta. He received his Ph.D. from UCLA in 2003. Sturtevant’s research on efficient methods for pathfinding gave him the opportunity to implement the pathfinding engine in BioWare’s upcoming game, “Dragon Age.”
MA4 Visual Recognition
Kristen Grauman (University of Texas at Austin) and Bastian Leibe (ETH Zurich)
The visual recognition problem is central to artificial intelligence. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial will overview computer vision algorithms for visual object recognition and image classification. We will introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers/students working in AI and robotics who would like to understand what methods and representations are available for these problems. Our intent is for attendees to walk away understanding what is and isn’t possible to do reliably today, and to gain key concepts that could be employed in their own systems or research.
Topics will include recognizing specific objects versu recognizing visual categories; global representations—face detection as a case study; local invariant features: detection and description; pose clustering, voting, Hough transform; indexing features, visual vocabularies; bag-of-words representations; constellation models, part-based models; implicit shape models; distance measures and kernels; supervised and weakly supervised model learning; and current challenges and research directions
We will assume some familiarity with probability, basic machine learning ideas, and linear algebra.
Kristen Grauman is an assistant professor in the Department of Computer Sciences at the University of Texas at Austin. She received her Ph.D. and M.S. degrees from MIT in the Computer Science and Artificial Intelligence Laboratory. Her research interests are in computer vision and machine learning.
Bastian Leibe is a postdoctoral research associate in the Computer Vision Laboratory at ETH Zurich. He received his M.Sc. degrees from the University of Stuttgart and Georgia Institute of Technology and his Ph.D. from ETH Zurich. His research interests are in computer vision, specifically on object recognition, reconstruction, and tracking.
MP1 External-Memory Graph Search
Stefan Edelkamp (Universität Dortmund), Eric Hansen (Mississippi State University), Shahid Jabbar (Universität Dortmund), and Rong Zhou (Palo Alto Research Center)
The scalability of A* and related graph-search algorithms is limited by the amount of memory that is available to store generated nodes for duplicate detection (that is, for recognition of when the same node is reached via different paths in a graph). Traditionally, the memory used in graph search is limited to RAM. But in the past few years, several researchers have shown that the scalability of A* and related graph-search algorithms can be dramatically improved by using external memory, such as disk, in addition to RAM. However, this requires very different search strategies to overcome the six orders-of-magnitude difference in random-access speed between RAM and disk.
This tutorial provides a comprehensive overview of recent and ongoing work on external-memory graph search, including duplicate-detection strategies (delayed, hash-based, and structured); external-memory versions of breadth-first search, A*, and related algorithms; and applications of external-memory graph search to AI planning, automated verification, and other search problems. Additional topics include integration of parallel and disk-based search, and external-memory pattern-database heuristics. The only prerequisite for the tutorial is familiarity with A* graph-search.
Stefan Edelkamp is a member of the faculty of the University of Dortmund. His research interests include AI heuristic search and its application to action planning and model checking. With Sven Koenig and Stefan Schroedl, he is coauthoring a book on the theory and applications of heuristic search.
Eric Hansen is an associate professor at Mississippi State University. His research interests include heuristic search and decision-theoretic planning using Markov decision processes. He is an associate editor of Artificial Intelligence journal, serves on the JAIR editorial board, and will be coprogram chair of ICAPS in 2008.
Shahid Jabbar is a research associate and Ph.D. student at the University of Dortmund. His research focus is on external-memory search algorithms for model checking and action planning, and he codeveloped the MIPS-XXL planning system that received a distinguished performance award in the recent International Planning Competition.
Rong Zhou is a member of the research staff of the Palo Alto Research Center (PARC), where he works in the embedded reasoning area of the Intelligent Systems Laboratory. His research interests include heuristic search and its applications, and his work has received two recent ICAPS best paper awards.
MP2 Introduction to Sketch Recognition
Tracy Hammond (Texas A&M University)
Sketch recognition is a growing topic of interest to many people as graphical diagrams pervade education, business, design, and many other domains. This tutorial will provide participants with an introduction to sketch recognition. Participants will learn about a myriad of sketch recognition algorithms that rely on drawing style, geometry, context, timing, bitmap, and other features, and the techniques used to recognize sketches from these features including rule-based, linear and quadratic, HMM, Bayesian network and other based classification algorithms. User interface techniques and issues will also be addressed, such as beautification and editing of diagrams. Participants will walk away with an overall understanding of the pitfalls and advantages of the different techniques and features as well as an idea of how to implement them. Participants will be provided with materials (that work either on provided tablets or their own nontablet computers) that allow for interactive learning throughout the tutorial.
Prerequisite knowledge: Participants are expected to be proficient in coding and computer science in general. Participants should have taken some higher-level computer science courses, such as artificial intelligence, user interfaces, or software engineering, to successfully grasp the conceptual material. No specific courses in machine learning or pattern recognition will be assumed.
Tracy Hammond is an assistant professor and director of the Sketch Recognition Lab at Texas A&M University where she also teaches a yearly graduate course on sketch recognition. She earned a B.A. in mathematics, a B.S. in applied mathematics, an M.S. in computer science, and an M.A. in anthropology, all from Columbia University. She earned her Ph.D. in computer science from MIT where she was a member of Randall Davis’ Design Rationale group. Previously, she was an instructor at Columbia University and an analyst at Goldman Sachs.
Brandon Paulson, Brian Eoff, Aaron Wolin, and Katie Dahmen are Ph.D. students and members of the Sketch Recognition Lab at Texas A&M University.
MP3 The Many Faces of Logistic Regression
David D. Lewis
This tutorial provides a broad-ranging introduction to logistic regression, a mainstay for supervised learning of classifiers and probabilistic modeling. The emphases will be on (1) presenting logistic regression from the point of view of several fields (statistics, machine learning, neural networks, the maximum entropy approach to computational linguistics, and uncertainty in AI, among others), and (2) discussing practical issues with applying logistic regression to real world data, particularly the sparse, high dimensional data that arises in text mining. The tutorial also indirectly serves as an overview of important concepts in modern machine learning, including Bayesian statistics, loss functions, regularization, optimization algorithms, and the extent to which effectiveness of trained models can be predicted.
Attendees should have a basic understanding of supervised machine learning, and an exposure to statistical concepts at the level required in a typical undergraduate computer science curriculum.
David D. Lewis, Ph.D. is a Chicago-based consultant on information retrieval, text mining, machine learning, and natural language processing. He previously held research positions at AT&T Labs, Bell Labs, and the University of Chicago. He has published more than sixty scientific papers, has six patents, and is a Fellow of the American Association for the Advancement of Science.
AAAI-08 Tutorial Program Cochairs
- Andrea Danyluk
Department of Computer Science
47 Lab Campus Drive
Williams College
Williamstown, MA 01267
413-597-2178
413-597-4250 (fax)
- Peter Stone
Department of Computer Sciences
The University of Texas at Austin
1 University Station C0500
Austin, Texas 78712-0233
(512) 471-9796
(512) 471-8885 (fax)