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cse.unsw.edu.au>========================================================================== 2nd CALL FOR PARTICIPATION Sixth Pacific Rim International Conference on Artificial Intelligence (PRICAI 2000 Co-located with RoboCup 2000) August 28 - September 1, 2000 Melbourne Convention Centre Cnr Flinders & Spencer Streets Melbourne, Victoria, Australia (http://www3.cm.deakin.edu.au/pricai/) =========================================================================== AUDIENCE This conference addresses a large and diverse constituency of Artificial Intelligence practitioners, researchers, educators, and users. The Pacific Rim International Conference on Artificial Intelligence (PRICAI) is the leading conference in the Pacific Rim region for presentation of research in Artificial Intelligence, including its applications to problems of social and economic importance. The objectives of PRICAI are: to provide a forum for the introduction and discussion of new research results, concepts, & technologies; to provide practising engineers exposure to and an evaluation of evolving research, tools, and practices; to provide the research community exposure to the problems of practical applications of AI; and to encourage the exchange of AI technologies and experience within Pacific Rim countries. PRICAI 2000 attempts to meet the needs of a large and diverse constituency, which includes researchers, practitioners, educators, and users. Topics of PRICAI 2000 include: Abduction Knowledge discovery & Data mining Adaptive interface Knowledge management Agents Knowledge representation AI architectures Logic programming Artificial life Machine learning Bayesian networks Machine translation Belief revision Mobile agents Cognitive modelling Model-based reasoning CSCW and AI Multimedia and AI Common sense reasoning Multiagent systems Computational complexity Music and AI Computational learning theory Natural language processing Complex systems Neural networks Computer-aided education Nonmonotonic reasoning Constraint satisfaction Ontological engineering Decision theory Planning Description logics Probabilistic reasoning Distributed AI Qualitative reasoning Emergent computing Real-time systems Foundation of AI Reinforcement learning Fuzzy Logics Robotics Game playing Scientific discovery Genetic algorithms Search Image processing Software engineering and AI Induction Spatial reasoning Industrial applications of AI Speech and spoken dialog understanding Intelligent CAD Temporal reasoning Intelligent information gathering Text analysis Intelligent learning environment Theorem proving Internet and AI User modelling Knowledge acquisition, Virtual reality modeling and validation Vision Knowledge-based systems Aspects of AI not otherwise listed Invited speakers - --------------------------------- (1) Professor Nick Jennings Dept. of Electronic Engineering Queen Mary & Westfield College University of London title:Automated Haggling: Building Artificial Negotiators (2) Prof. Shun-ichi Amari RIKEN Brain Science Institute Director of Brain-Style Information Systems Research Group Laboratory for Information Synthesis, Head Japan title:Information Geometry of Neural Networks (3) Randy Goebel Professor of Department of Computing Science University of Alberta title:Knowledge representation, belief revision, and the challenge of optimality. (4) Qiang Yang Professor of the School of Computing Science Simon Fraser University (SFU), British Columbia Canada title:New-generation Search and Information Retrieval on the Web Contact Information - ----------------- For PC matters, please contact: Riichiro Mizoguchi PC Co-Chair Email: mizMail to author|Respond to list|Read more issues|LINGUIST home page|Top of issue
cse.unsw.edu.au>TUTORIALS AT PRICAI2000 on 28 & 29 August 2000, before the conference (30 August - 1 September 2000) T1: The role of Artificial Intelligence in Knowledge Management by Eric Tsui (28/8/2000 Morning) Summary Knowledge Management (KM) is an emerging field rapidly gaining momentum both in the research arena and the business community. This tutorial begins with a review of the dominant trends in the development of Corporate Knowledge Management by linking recent work to the background of researchers. A number of AI areas/techniques are finding their way as strong enablers of KM. Most noticeably, Intelligent Agents, Information Filtering and Ontologies. Common KM applications will be described and these include, among others, knowledge maps and corporate memories in product development, software re-use, business processes and knowledge communities. Challenges and predictions for KM will also be outlined. This tutorial is especially suited to practitioners and researchers who want to learn about the field of KM and the major contributions of AI research and applications to this new field. Biography of presenter Eric Tsui joined the Expert Systems Group of Computer Sciences Corporation (CSC) in 1989 after years of academic research in automated knowledge acquisition, case-based reasoning and knowledge engineering tools. He practices KM to CSC clients as well as designs and delivers KM courses for two universities. He is also the primary guest editor of the KBS Journal (Elsevier)'s forthcoming Special Issue on AI in KM. His qualifications include B.Sc.(Hons.), PhD, MBA and is an adjunct of the University of Sydney and University of Technology, Sydney. T2: Language Technology: Applications and Techniques by Associate Professor Robert Dale (28/8/2000 Afternoon) Context and Motivation Language Technology is the new millenniums practically-focussed rebirth of natural language processing, covering applications from optical character recognition to sophisticated spoken language dialog systems and intelligent search engines. Language Technology is widely perceived by the IT industry to be a fundamental enabling technology that will both enable smarter interfaces and provide assistance in overcoming the information overload of the Internet age. Tutorial Aims and Content This tutorial aims to provide the attendee with a broad awareness of actual and potential Language Technology applications, along with a framework for thinking about these applications in terms of the linguistic resources they need. Attendees will acquire an understanding of the scale of development required for different kinds of applications, along with an appreciation of what constitutes a feasible application. Frequent reference will be made to commercial applications, with corresponding critiques aimed at showing how to assess claims made by vendors. Biography of Presenter Robert Dale has an international research reputation in natural language processing, and particularly in natural language generation; he has presented numerous tutorials on these topics at international conferences. He is author of over 50 journal and conference papers, and is author and editor of a number of books in the area; most recently Building Natural Language Generation Systems (Reiter and Dale 2000; Cambridge University Press) and the forthcoming Handbook of Natural Language Processing: Tools and Techniques (Dale, Moisl and Somers [eds], Dekker Publishing). He teaches part time at Macquarie University, and is Director of Language Technology Pty Ltd, a consultancy focussing on cutting-edge speech and language applications. T3: Introduction to Minimum Length Encoding Inference by Dr. David Dowe (29/8/2000 Morning) Summary The tutorial will be on Minimum Length Encoding, encompassing both Minimum Message Length (MML) and Minimum Description Length (MDL) inductive inference. This work is information-theoretic in nature, with a broad range of applications in machine learning, statistics, "knowledge discovery" and "data mining". We discuss the following topics: statistical parameter estimation; mixture modelling (or clustering) of continuous, discrete and circular data; clustering with correlated attributes; learning decision trees; learning decision trees with Markov model leaf regressions; learning probabilistic finite state machines; and possibly other problems if time permits. We will also show the successes of MML compared to other methods both in fitting polynomial functions and in modelling and fitting an alternating binary process. MML is statistically consistent and efficient, meaning that it converges as quickly as is possible to any true underlying data-generating process. It is also invariant under 1-to-1 re-parameterisation of the problem and has a better than good track record in problems of machine learning, statistics and ``data mining''. Some of the above machine learning techniques will then be applied to real-world problems, such as protein structure prediction and the human genome project, lossless image compression, exploration geology, business forecasting, market inefficiency and natural language. Passing mention will be made of foundational issues such as connections to Kolmogorov-Solomonoff-Chaitin complexity (see recent special issue of the Computer Journal), universal modelling and (probabilistic) prediction. Biography of presenter Dr David Dowe works primarily with Lloyd Allison, Trevor Dix, Chris Wallace and others in the Minimum Message Length (MML) group at the School of Computer Science and Software Engineering at Monash University. Most of his work for the past 9 years has been in the theory and applications of the (information-theoretic) MML principle of statistical and inductive inference and machine learning (and "knowledge discovery" and "data mining"), a principle which dates back to Wallace and Boulton (Comp. J., 1968), and which has been surveyed more recently in Wallace and Freeman (J. Roy. Stat. Soc., 1987) and Wallace and Dowe (Comp. J., 1999). David was Program Chair of the Information, Statistics and Induction in Science (ISIS) conference, held in Melbourne, Australia on 20-23 August 1996; attended by R. J. Solomonoff, C. S. Wallace, J. J. Rissanen, J. R. Quinlan, Marvin Minsky, and others. T4: Case-Based Reasoning in the Finance and Service Sectors by Ian Watson (29/8/2000 Morning) Summary Case-based reasoning (CBR) has long been successfully used in customer support applications, in particular in help-desks for the technical support of products and services via the Internet. Therefore, it is a natural extension for CBR to support the selection, customisation and sale of products and services in e-commerce systems in what is being termed customer relationship management (CRM). This tutorial will introduce attendees to the concepts underpinning CBR and illustrate why its concepts of similarity, reuse, adaptation and retention are so appropriate to CRM. A framework for the delivery of intelligent services for e-commerce systems based on CBR, XML and Java will be illustrated with fielded systems operating in many application areas, including: finance, real estate, travel agencies and used car sales. Biography of presenter Ian Watson is a Senior Lecturer in the Department of Computer Science at the University of Auckland in New Zealand. Ian was the founder of AI-CBR (www.ai-cbr.org) the leading Internet site for CBR researchers and developers and is the author of "Applying Case-Based Reasoning: techniques for enterprise systems" the first book on the application of CBR. Ian was awarded a "Distinguished Paper" award at IJCAI-99 for work on a distributed CBR system for engineering sales support and will co-chair the 4th. International Conference on Case-Based Reasoning (ICCBR01) in July 2001 in Vancouver. T5: Designing Human-Centered Autonomous Agents by Gregory Dorais and David Kortenkamp (29/8/2000 Afternoon) Summary This tutorial will present requirements and architectural guidelines for designing autonomous systems that include humans and autonomous agents who interact to achieve complex goals. We call such systems human-centered autonomous agents. This tutorial draws relevant research from each of these areas. We will particularly focus on identifying guidelines for autonomous agents that will enable users, other software agents, or the agent itself to dynamically change the "level of autonomy" within a spectrum ranging from complete human control to complete autonomous control. We refer to this capability as adjustable autonomy and it is a key feature of human-centered autonomous agents. In this tutorial we will present the state-of-the-art in human-centered autonomous agents and give guidelines and a methodology for developing such agents. These will be supported by a running example. We will finish by describing some applications of human-centered autonomous agents. Our goal is to provide insight to agent designers on how to create autonomous systems that minimize the necessity for human interaction, but maximize the capability for humans to interact at whatever level of control is most appropriate. Biography of presenters Dr. Gregory Dorais is a computer scientist in the Autonomy and Robotics Area in the Computational Sciences Division of the NASA Ames Research Center. He received both his Ph.D. and M.S. in Computer Science from the University of Michigan and his B.S. in Management Information Systems from Oakland University. He is a co-principal investigator of the "Intelligent Deployable Execution Agent" project at NASA. Dr. Dorais was the integration lead of the Remote Agent experiment for the Deep Space 1 spacecraft which was the first AI agent-controlled spacecraft featuring an on-board planner and a model-based inference system. He has performed autonomous rover research at JPL and remote sensing research at General Motors Research. Dr. Dorais co-organized the 1999 AAAI Spring Symposium on "Agents with Adjustable Autonomy" and the 1999 IJCAI workshop on "Adjustably Autonomous Systems". He was on the program committee of the 1999 Autonomous Agents workshop on "Autonomy Control Software". David Kortenkamp is a senior scientist with Metrica Inc./TRACLabs supporting NASA Johnson Space Center. He has a PhD and MS in computer science and engineering from the University of Michigan and a BS in computer science from the University of Minnesota. At NASA, Dr. Kortenkamp is co-principal investigator (with Dr. Gregory Dorais) of the "Human-Centered Autonomous Agents" project. He has also co-organized a AAAI Spring Symposium on "Agents with Adjustable Autonomy" and an IJCAI workshop on "Adjustable Autonomy Systems". He is guest editor with Henry Hexmoor of an upcoming JETAI special issue on Autonomous Control Systems and is associate editor of the MIT Press series on Intelligent Robotics and Autonomous Agents. Dr. Kortenkamp has given numerous invited talks on the subject of human-centered autonomous agents including a Robotics Institute Seminar at Carnegie Mellon University and an Artificial Intelligence Seminar at the University of Virginia. He is on the program committee of Autonomous Agents 2000 and AAAI-2000.Mail to author|Respond to list|Read more issues|LINGUIST home page|Top of issue