Stefan Decker and Steffen Staab
Knowledge management (KM) is a discipline with the purpose of managing the knowledge assets of organizations. Though KM is deeply rooted in business management, IT support may add an enormous lever-age for the effortless creation, conservation, sharing, and exploitation of organizational knowledge. AI has a long tradition in creating, interacting with and managing of knowledge and has developed many techniques that may propel this knowledge management cycle inside an organization. In the tutorial, we will give an introduction in intelligent IT support for Knowledge Management that individual and communities of users in knowledge-intensive organizations may benefit from.
Prerequisites: Participants should have some general IT knowledge and basic knowledge about AI.
Stefan Decker is a postdoctoral fellow at the department of computer science at Stanford University, where he works on ontology articulation. His research interests include knowledge representation, and database systems for the Semantic Web, information integration and translation, and ontology articulation and merging. See http://www-db.stanford.edu/~stefan and http://www.SemanticWeb.org.
Steffen Staab is assistant professor at the Univ. of Karlsruhe and working part-time at Ontoprise, a company he has co-founded. Steffen holds M.S.E. and Dr.rer.nat. degrees from the University of Pennsylvania and Freiburg University, respectively. He has been working and publishing on knowledge management, text mining, knowledge representation and the Semantic Web. See http://www.aifb.uni-karlsruhe.de/~sst/.
Tuomas Sandholm
In multiagent systems for agent-mediated electronic commerce, computational agents find contracts on behalf of the real-world parties that they represent. This saves human negotiation time, and computational agents are often better at finding beneficial deals in combinatorially and strategically complex settings. Applications include supply chain coordination, electricity markets, bandwidth allocation, vehicle routing among dispatch centers, and resource allocation in distributed operating systems, to name just a few.
A key goal is
to design open distributed systems in a principled way that leads to globally
desirable outcomes even though every participating agent only considers its own
good and may act insincerely. The tutorial covers relevant AI and game theory
topics in voting, auctions (also multi-unit, multi-item, and many-to-many
exchanges), and automated contracting. Emphasis is given to fundamental results
and algorithms. Effects of computational limitations (agents’ bounded
rationality) are discussed as a key feature that has not received adequate
attention. Implementation
experiences will be shared, and real world applications presented.
No background is required in economics, game theory or multiagent systems.
Tuomas Sandholm is Associate Professor in the Computer Science Department at Carnegie Mellon University. He received the Ph.D. and M.S. degrees in computer science from UMass, Amherst in 1996 and 1994. He earned an M.S. (B.S. included) with distinction in Industrial Engineering and Management Science from the Helsinki University of Technology, Finland, in 1991. He has over ten years of experience building multiagent systems. He has co-developed two fielded AI systems, founded two companies in the area of electronic commerce, and published over 115 technical papers. See http://www.cs.wustl.edu/~sandholm/.
Miroslav Kubat
Artificial
neural networks rank among the most popular tools in pattern recognition. Their
widespread use was boosted by algorithms for learning from examples. A
challenging research issue is how to establish, for a given task, appropriate
neural architecture: small networks are prone to get trapped in local minima,
whereas large networks tend to overfit the training examples.
Historically,
researchers relied on trial-and-error procedures, experimenting with several
topologies, and then selecting the one that best satisfied predefined criteria.
In the 1990s, more systematic techniques emerged. They can roughly be divided
into three categories. Search-based strategies that exploit AI-search
techniques, including the genetic algorithm; logic-based strategies that
utilize prior knowledge expressed as production rules or decision trees; and
piecemeal strategies that focus on one neuron at a time.
The tutorial begins with a systematic introduction into networks consisting of mutually interconnected layers of neurons. Then, the techniques for their architectural design will be investigated. Due attention is devoted to experience from major projects.
No special prerequisite knowledge is required.
Miroslav Kubat is Associate Professor of Computer Science at the University of Louisiana at Lafayette. His research focuses primarily on Machine Learning and Neural Networks. Prior to coming to the U.S., he held positions in Austria, Canada, and the Czech Republic. He has published about 60 scientific papers and co-edited (with R. Michalski and I. Bratko) the book Machine Learning and Data Mining: Methods and Applications.
Carla P. Gomes, Tad Hogg, Toby
Walsh, and Weixiong Zhang
This tutorial
will present an exciting area combining concepts from theoretical physics and
artificial intelligence. We will show how the study of phase transition,
structure, and related phenomena is changing the way we characterize the
computational complexity of combinatorial problems, beyond the notion of
worst-case complexity. Furthermore, we will discuss how we can use tools from
statistical physics to provide a much more detailed description of a problem’s
complexity and how we can leverage such insights into the design of search
algorithms.
We will
describe phase transition behavior observed in a number of different decision
problems such as SAT, graph coloring, and number partitioning, as well as
optimization problems such as TSP and maximum SAT, and in other complexity
classes like P and PSpace. The second part of the tutorial will cover recent
work connecting structural features of problems with phase transition phenomena
and computational complexity. Topics
covered will include constrainedness, backbone structure, and small world
topology. We will also discuss how to exploit structure and randomness in
problems using restart strategies and, more generally, portfolios of
algorithms.
The tutorial is aimed at the general AI audience. Familiarity with some basic concepts of combinatorial optimization, probability theory, and computational complexity is desirable but not essential. Please visit http://www.cs.wustl.edu/~zhang/links/ijcai-phase-transitions. html.
Carla P. Gomes is the Director of the Intelligent Information Systems Institute at Cornell University. Her research has covered several areas in artificial intelligence and computer science, including planning and scheduling, integration of CSP and OR techniques for solving combinatorial problems, and algorithm portfolios.
Tad Hogg is on the research staff of Xerox PARC. His research interests include multiagent systems, smart matter, and the relation between physics and computation, including analogies with physical phase transitions found in combinatorial search.
Toby Walsh is an EPSRC Advanced Research Fellow at the Department of Computer Science (York). He has worked extensively on phase transition behavior in a number of different areas including: satisfiability, constraint satisfaction, traveling salesperson problems, and number partitioning.
Weixiong Zhang is an associate professor at Washington University in St. Louis. His primary research interests include multiagent systems, heuristic search and combinatorial optimization, especially phase transition phenomena and approximation methods that exploit phase transitions.
Dan Moldovan and Sanda Harabagiu
Question
Answering (QA) is a fast growing area of research with tremendous commercial
potential. The problem of QA is to find answers to open-domain questions by
searching a large collection of documents. Unlike Internet search engines, QA
systems provide short, relevant answers to questions.
The recent
explosion of information available on the World Wide Web makes question
answering a compelling framework for finding information that closely matches
user needs. Due to the fact that both questions and answers are expressed in
natural language, QA methodologies deal with language ambiguities and
incorporate NLP techniques.
The tutorial presents a survey of the most performant open-domain QA systems architectures and the basic techniques employed to build them. Topics include: answer taxonomies, answer processing, document retrieval, answer extraction and ranking, accuracy performance and speed performance.
Dan Moldovan is a Professor of Computer Science and Engineering at Southern Methodist University, Dallas, Texas. Prior to this he was a faculty at the University of Southern California, Member of Technical Staff at Bell Laboratories, and a Program Director at the National Science Foundation. Dr. Moldovan received a PhD from Columbia University in 1978.
Sanda Harabagiu is an Assistant Professor in the Department of
Computer Science and Engineering at Southern Methodist University, Dallas,
Texas. She received a PhD in Computer Engineering from the University of
Southern California, Los Angeles in 1997 and a Doctorate in Computer Science form
the University of Rome "Tor Vergata", Italy in 1994. Dr. Harabagiu is a
recipient of the National Science Foundation CAREER award.
Benjamin Grosof and Yannis
Labrou
Background:
Electronic markets (e-markets) for the buying and selling of goods and services
over the Web are a fast-growing, multi-billion-dollar segment of the world
economy. Relevant knowledge-based techniques draw on several areas of AI:
knowledge representation and reasoning, learning, and communication. As more
knowledge-based pieces of e-commerce have developed, issues are arising of how
to put them together into overall functioning markets — largely, via forms of
agent communication. E-markets include infrastructural and intermediary
services, e.g., for yellow pages, catalogs, shopping search, advertising, sales
assistants, brokers/aggregators, infomediaries, reputation/trust,
authentication, and payments. Intelligent software agents in this context are
autonomous, cooperating processes that use rich agent communication languages
to exchange information and knowledge and to coordinate their activities.
This tutorial
will discuss existing techniques and their theory, currently identified
challenges, standardization efforts and near-future opportunities for practical
applications of agent communication in knowledge-based e-markets. Here,
knowledge-based techniques for agent
communication, ontologies, business rules, and information integration are of
rising interest, in part due to the rise of XML, and have started having
practical impact on real e-markets. The tutorial includes a brief review of
several agent-based projects that are using these emerging standards.
Prerequisites: Basic general AI knowledge, in particular especially the basics of rule-based knowledge representation, is assumed.
Benjamin Grosof is Assistant Professor in Information Technology at the MIT Sloan School of Management. His research addresses e-commerce and Web technology, combining agent communication, XML, and knowledge representation for applications in contracting, negotiation, and business policies. He is PI currently for a project in the DARPA Agent Markup Language (DAML) initiative, designing knowledge-level techniques to realize the vision of the Semantic Web. Previously, he was a senior research scientist at IBM T.J. Watson Research Center. While at IBM, most recently he founded and led a project on Business Rules for E-Commerce. This produced IBM CommonRules (V2.1 currently on IBM alphaWorks), which pioneered XML agent communication of inter-operable business rules with conflict handling. He co-led its application piloting for rule-based XML agent contracting in EECOMS, a $29Million NIST industry consortium project on manufacturing supply chain management. He holds a PhD from Stanford University in Computer Science, with specialty AI, and a BA from Harvard University in Applied Mathematics, with specialty economics and management science. He is author of over 30 refereed publications, two major software releases, and one patent; and co-chaired the AAAI Conference Workshops on AI in E-Commerce (1999) and Knowledge-Based E-Markets (2000). Email: bgrosof@mit.edu. Home Page: http://www.mit.edu/~bgrosof
John E. Laird and Michael van
Lent
Computer
games are becoming a major application area of AI. Although games have traditionally used very simple AI, the
computer game industry is seeing a significant increase in available CPU power
as graphics processing has moved into special purpose processors. We can expect
a significant increase in the complexity and importance of AI with computer
games.
This tutorial
provides a broad overview of computer and video games for the AI researcher
interested in learning more about the business and technology of computer
games. The major topics that are covered include:
Michael van Lent received his PhD from the University of Michigan in 2000. His research interests include machine learning and artificial intelligence in computer games. His game-related activities include the development of a senior-level class titled "Computer Game Design and Implementation", helping to organize the 1999, 2000 and 2001 AAAI Spring Symposia on AI and Interactive Entertainment, and speaking at the 1999 Game Developer's Conference.
John E. Laird is a Professor of Electrical Engineering and Computer Science at the University of Michigan. He received his PhD in Computer Science from Carnegie Mellon University in 1983. He is one of the creators of the Soar architecture and leads its continued development and evolution. He was elected as a Fellow of AAAI in 1995.
Aaron Sloman and Matthias
Scheutz
This
tutorial, presented by tutors with considerable experience in both philosophy
and AI, will introduce philosophical problems relevant to the goals and
methodology of AI as science and AI as engineering, including the contribution
of AI to the study of mind. The mode of presentation will be a mixture of
lectures and interactive discussions, addressing the following topics:
A more
detailed overview of the tutorial will be available online at http://www.cs.bham.ac.uk/~axs/ijcai01/.
Prerequisite knowledge: Knowledge of AI and experience of software development will help.
Aaron Sloman (http://www.cs.bham.ac.uk/~axs/) is a professor of AI & Cognitive Science, University of Birmingham. BSc Physics, mathematics, 1956; DPhil Philosophy, 1962. Rhodes Scholar. Fellow of AAAI, AISB and ECCAI. Research: vision, spatial reasoning, architectures for human-like agents, emotions and related philosophical topics. Toolkit for cognitively rich agents. Author: The Computer Revolution in Philosophy (Harvester Press, 1978) and many papers on AI, Cognitive Science and Philosophy.
Matthias Scheutz (http://www.cs.bham.ac.uk/~mxs/) is a research Fellow in AI, University of Birmingham, Assistant Professor in AI, University of Notre Dame (on leave). Ph.D. Philosophy, 1995; Ph.D. Cognitive Science and Computer Science, 1999 Organizer NTCS'99: "Computationalism - The Next Generation" ( http://www.univie.ac.at/cognition/conf/ntcs99/) Co-founder of Austrian Society for Cognitive Science. Research: philosophy of cognitive science and AI (the nature of implementation), behavior based robotics, evolvable agent architectures. Author of several conference and journal papers on AI and the philosophy of cognitive science.
Holger H. Hoos and Thomas
Stützle
Stochastic
search algorithms have been shown to outperform their deterministic
counterparts in a number of interesting application domains. They are becoming
increasingly important and popular for solving computationally hard
combinatorial problems from various domains of AI and Operations Research, such
as planning, scheduling, constraint satisfaction, and satisfiability.
In this
tutorial we will introduce stochastic search algorithms and characterize them
as instances of the more general class of Las Vegas algorithms. We will cover
local search algorithms, including stochastic hill-climbing, simulated
annealing, tabu search, evolutionary algorithms, and ant colony optimization,
as well as randomized systematic search algorithms. For exemplifying these
algorithms, we will mainly use the satisfiability problem in propositional
logic (SAT) and the Traveling Salesman problem (TSP), which both play a
central role in the design, implementation, and analysis of algorithmic ideas.
We will also address the empirical analysis of Las Vegas algorithms and present
case studies demonstrating the successful application of stochastic search
algorithms to various problem domains.
Prerequisite knowledge: The attendees should have an interest in computationally hard combinatorial problems. Basic knowledge in standard AI search problems as well as a basic knowledge of search methods would be an advantage but is not a necessary prerequisite.
Holger H. Hoos is an Assistant Professor at the Computer Science Department of the University of British Columbia (Canada), where he is a co-founder of the Bioinformatics, Empirical & Theoretical Algorithmics Laboratory (BETA-Lab) and a member of the Laboratory for Computational Intelligence (LCI). He received his PhD from the Computer Science Department at TU Darmstadt (Germany).
Thomas Stützle is an Assistant Professor at the Computer Science Department of Darmstadt University of Technology, where he is local coordinator of the Metaheuristics Network. He holds a MSc in Economics and Industrial Engineering from TU Karlsruhe and a PhD in Computer Science from TU Darmstadt and has been a Marie-Curie fellow at IRIDIA (Brussels, Belgium).
Anthony Jameson
Interactive
systems that adapt to their users have been gaining rapidly in practical
importance, for example in the areas of e-commerce and web-based information
access. Relevant terms include personalization, personal assistants, adaptive
interfaces, user modeling, and student modeling. The AI techniques employed
include machine learning techniques, probabilistic and decision-theoretic
approaches, and logic-based methods.
Although
user-adaptive systems take many different forms, there are a number of
questions that must be addressed in the design of any such system:
What functions are to be served by the adaptation?
What properties of the user should be modeled?
What input data about the user should be obtained, and how?
What techniques should be employed to make inferences about the
user?
How should decisions about appropriate adaptive system behavior
be made?
What empirical studies should be conducted?
For each of
these questions, we will systematically examine the main answers that have been
worked out so far in research and practice. The discussion will integrate the
results of previous experience in many different domains from an AI
perspective, and it will refer throughout to concrete system examples.
Prerequisite Knowledge: This tutorial will be accessible to all IJCAI-01 attendees.
Anthony Jameson is a senior researcher at the German Research
Institute for Artificial Intelligence (DFKI) and adjunct professor at the
International University in Germany. He has published widely on user-adaptive
systems since the early 1980s. He was program co-chair of the Sixth
International Conference on User Modeling. Email: jameson@cs.uni-sb.de,
Homepage: http://www.cs.uni-sb.de/users/jameson/.