Lecture Notes in Artificial Intelligence Edited by J G Carbonell and J Siekmann Subseries of Lecture Notes in Computer Science 2671 Berlin Heidelberg New York Barcelona Hong Kong London Milan Paris Tokyo Yang Xiang Brahim Chaib-draa (Eds.) Advances in Artificial Intelligence 16th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2003 Halifax, Canada, June 11-13, 2003 Proceedings 13 Series Editors Jaime G Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jăorg Siekmann, University of Saarland, Saarbrăucken, Germany Volume Editors Yang Xiang University of Guelph Department of Computing and Information Science College of Physical and Engineering Science Guelph, Ontario, Canada N1G 2W1 E-mail: yxiang@cis.uoguelph.ca Brahim Chaib-draa Universit´e Laval D´ept Informatique-G´enie Logiciel Pavillon Pouliot, Ste-Foy, PQ, Canada, G1K 7P4 E-mail: Chaib@ift.ulaval.ca Cataloging-in-Publication Data applied for A catalog record for this book is available from the Library of Congress Bibliographic information published by Die Deutsche Bibliothek Die Deutsche Bibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the Internet at CR Subject Classification (1998): I.2 ISSN 0302-9743 ISBN 3-540-40300-0 Springer-Verlag Berlin Heidelberg New York This work is subject to copyright All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag Violations are liable for prosecution under the German Copyright Law Springer-Verlag Berlin Heidelberg New York, a member of BertelsmannSpringer Science+Business Media GmbH http://www.springer.de © Springer-Verlag Berlin Heidelberg 2003 Printed in Germany Typesetting: Camera-ready by author, data conversion by DA-TeX Gerd Blumenstein Printed on acid-free paper SPIN: 10927465 06/3142 543210 Preface The AI 2003 conference was the 16th in the series of artificial intelligence conferences sponsored by the Canadian Society for Computational Studies of Intelli´ gence (CSCSI)/Soci´et´e Canadienne pour l’Etude de l’Intelligence par Ordinateur (SCEIO) The conference showcases the excellent research work done by Canadians and their international colleagues As in the case of many past Canadian AI conferences, AI 2003 was organized in conjunction with its sister Canadian conferences, Vision Interface (VI) and Graphics Interface (GI), enriching the experience for all participants The conferences were held on the campus of Dalhousie University, at Canada’s largest Atlantic port city, Halifax This year, we received a record number of paper submissions A total of 116 abstracts were received, out of which 106 papers were submitted by the due date As at past conferences, there was strong international participation Among the submitted papers, about 41% were from non-Canadian researchers From the 106 papers, we accepted 30 full papers and 24 short papers Following the success in AI 2002, the Graduate Student Symposium was continued in AI 2003, with 11 extended abstracts accepted from 16 submissions All these accepted papers are included in this volume They cover a wide range of topics, including knowledge representation, search, constraint satisfaction, natural language, machine learning and data mining, reasoning under uncertainty, agent and multiagent systems, AI and Web applications, AI and bioinformatics, and AI and E-commerce We invited three distinguished researchers representing three very active subfields of AI: Victor Lesser (multiagent systems), Tom Mitchell (machine learning), and Pierre Baldi (AI and bioinformatics) The extended abstracts of their invited talks also appear in this volume Many contributed to the organization of AI 2003 Members of the Program Committee made helpful suggestions on the conference organization They and the associated referees carefully and critically reviewed all submissions and ensured a high-quality technical program The National Research Council of Canada and the Canadian Society for Computational Studies of Intelligence provided travel support for the Graduate Student Symposium CSCSI’s past president Bob Mercer and president Bruce Spencer gave us much guidance whenever needed The conference chair Charles Ling and the local organizer Malcolm Heywood attended to many organizational details We thank the invited speakers, all authors who submitted their work to AI 2003, and the conference participants We thank the AI-GI-VI Steering Committee and the organizers of GI and VI for their cooperation Our home institutions, the University of Guelph and Laval University, and the host institution of the conference, Dalhousie University, provided much assistance and support Alfred Hofmann and Ursula Barth at Springer-Verlag assisted the publication of this volume Graduate students Feng Zou, Junjiang Chen, Xiaoyun Chen and Xiangdong An assisted in devel- VI Preface oping and maintaining the program management website and in preparing the proceedings June 2003 Yang Xiang Brahim Chaib-draa Executive Committee Conference Chair: Program Co-Chairs: Local Organizer: Charles Ling (U Western Ontario) Yang Xiang (U Guelph) Brahim Chaib-draa (Laval U.) Malcolm Heywood (Dalhousie U.) Program Committee Aijun An (York U.) Cory Butz (U Regina) Nick Cercone (Dalhousie U.) David Chiu (U Guelph) Jim Delgrande (Simon Fraser U.) Jorg Denzinger (U Calgary) Renee Elio (U Alberta) Richard Frost (U Windsor) Ali Ghorbani (U New Brunswick) Scott Goodwin (U Windsor) Jim Greer (U Saskatchewan) Gary Grewal (U Guelph) Howard Hamilton (U Regina) Bill Havens (Simon Fraser U.) Michael Horsch (U Saskatchewan) Finn Jensen (Aalborg U.) Stefan Kremer (U Guelph) James Little (U British Columbia) Stan Matwin (U Ottawa) Gord McCalla (U Saskatchewan) Bob Mercer (U Western Ontario) Evangelos Milios (Dalhousie U.) Guy Mineau (U Laval) Eric Neufeld (U Saskatchewan) Petra Perner (IBaI Leipzig) David Poole (U British Columbia) Fred Popowich (Simon Fraser U.) Gregory Provan (Rockwell) Dale Schuurmans (U Waterloo) Weiming Shen (National Research Council Canada) Danel Silver (Acadia U.) Bruce Spencer (National Research Council Canada and U New Brunswick) Deb Stacey (U Guelph) Stan Szpakowicz (U Ottawa) Andre Trudel (Acadia U.) Peter van Beek (U Waterloo) Julita Vassileva (U Saskatchewan) Michael Wong (U Regina) Jia You (U Alberta) Eric Yu (U Toronto) Kaizhong Zhang (U Western Ontario) Additional Reviewers Mohamed Aoun-allah, Gilbert Babin, Behnam Bastani, Julia Birke, Pierre Boulanger, Caropreso, Ralph Deters, Dan Fass, Julian Fogel, Fr´ed´erick Garcia, Ali Ghodsi, Daniel Gross, Jimmy Huang, Andrija Ifkovic, Nadeem Jamali, Anthony Kusalik, Sonje Kristtorn, Lang, Lingras, Lin Liu, Yang Liu, Sehl Mellouli, Ronnie Mueller, Xiaolin Niu, Relu Patrascu, Elhadi Shakshuki, Pascal Soucy, Finnegan Southey, Sykes, Davide Turcato, Hussein Vastani, Qian Wan, Steven Wang, Yao Wang, Pinata Winoto, Sadok Ben Yahia, Harry Zhang Sponsor National Research Council of Canada Canadian Society for Computational Studies of Intelligence Table of Contents Invited Talks Experiences Building a Distributed Sensor Network Victor Lesser Artificial Intelligence and Human Brain Imaging Tom M Mitchell Machine Learning Methods for Computational Proteomics and Beyond Pierre Baldi Full Papers Knowledge Representation On the Structure Model Interpretation of Wright’s NESS Test Richard A Baldwin and Eric Neufeld Answer Formulation for Question-Answering 24 Leila Kosseim, Luc Plamondon, and Louis-Julien Guillemette Pattern-Based AI Scripting Using ScriptEase 35 Matthew McNaughton, James Redford, Jonathan Schaeffer, and Duane Szafron Enumerating the Preconditions of Agent Message Types 50 Francis Jeffry Pelletier and Ren´ee Elio Search Monadic Memoization towards Correctness-Preserving Reduction of Search 66 Richard Frost Searching Solutions in the Crypto-arithmetic Problems: An Adaptive Parallel Genetic Algorithm Approach 81 Man Hon Lo and Kwok Yip Szeto Stochastic Local Search for Multiprocessor Scheduling for Minimum Total Tardiness 96 Michael Pavlin, Holger Hoos, and Thomas Stă utzle X Table of Contents Constraint Satisfaction A Graph Based Backtracking Algorithm for Solving General CSPs 114 Wanlin Pang and Scott D Goodwin Iterated Robust Tabu Search for MAX-SAT 129 Kevin Smyth, Holger H Hoos, and Thomas Stă utzle Scaling and Probabilistic Smoothing: Dynamic Local Search for Unweighted MAX-SAT 145 Dave A D Tompkins and Holger H Hoos A Comparison of Consistency Propagation Algorithms in Constraint Optimization 160 Jingfang Zheng and Michael C Horsch Machine Learning and Data Mining Discovering Temporal/Causal Rules: A Comparison of Methods 175 Kamran Karimi and Howard J Hamilton Selective Transfer of Task Knowledge Using Stochastic Noise 190 Daniel L Silver and Peter McCracken Efficient Mining of Indirect Associations Using HI-Mine 206 Qian Wan and Aijun An Case Authoring from Text and Historical Experiences 222 Marvin Zaluski, Nathalie Japkowicz, and Stan Matwin AI and Web Applications Session Boundary Detection for Association Rule Learning Using n-Gram Language Models 237 Xiangji Huang, Fuchun Peng, Aijun An, Dale Schuurmans, and Nick Cercone Negotiating Exchanges of Private Information for Web Service Eligibility 252 Keping Jia and Bruce Spencer Post-supervised Template Induction for Dynamic Web Sources 268 Zhongmin Shi, Evangelos Milios, and Nur Zincir-Heywood Summarizing Web Sites Automatically 283 Yiquing Zhang Zhang, Nur Zincir-Heywood, and Evangelos Milios Learning Coordination in RoboCupRescue S´ebastien Paquet DAMAS laboratory, Laval University, Canada spaquet@damas.ift.ulaval.ca Abstract In this abstract, we present a complex multiagent environment, the RoboCupRescue simulation, and show some of the learning opportunities for the coordination of agents in this environment Introduction A fundamental difficulty in cooperative multiagent systems is to find how to efficiently coordinate agents’ actions in order to enable them to interact and achieve their tasks proficiently One solution for this problem is to give the agents the ability to learn how to coordinate their actions This type of solution is well suited for complex environments as RoboCupRescue, because the designer does not have to come up with all the rules for all possible situations RoboCupRescue The goal of the RoboCupRescue simulation project is to build a simulator of rescue teams acting in large urban disasters [3] More precisely, this project takes the form of an annual competition in which participants are designing rescue agents that are trying to minimize damages, caused by a big earthquake, such as civilians buried, buildings on fire and blocked roads The RoboCupRescue simulation is a complex multiagent environment that has some major issues like: agents’ heterogeneity, long-term planning, emergent collaboration and information access [2] In the simulation, participants have approximately 30 agents of six different kinds to manage and each of them has different capabilities, for instance, AmbulanceTeam agents can rescue civilians, FireBrigade agents can extinguish fires and PoliceForce agents can clear roads As we can see, this multiagent system is composed of heterogenous agents, having complementary capabilities, that will have to cooperate and coordinate their actions to accomplish their goals Coordination Approaches and Learning Opportunities Solutions to coordination problems can be divided in three general classes [1]: those based on communication, those based on convention and those based on Y Xiang and B Chaib-draa (Eds.): AI 2003, LNAI 2671, pp 627–628, 2003 c Springer-Verlag Berlin Heidelberg 2003 628 S´ebastien Paquet learning In the RoboCupRescue environment, approaches based on communication are not appropriate, because the constraints on communication are too restrictive We could use an approach based on convention and presently it is the most used approach, because it is the simplest However, since the RoboCupRescue simulation is a complex environment in which many different situations could occur, it becomes very difficult to find all the right conventions for all the possible situations In such an environment, learning becomes interesting because it removes from the designer the hard job of defining all coordination procedures required for all possible situations We think that the RoboCupRescue environment is a good testbed for the study of coordination learning techniques in a complex real-time environment and we will show some of those learning opportunities in the next paragraphs The first learning approach consist of learning how to use the communication channel efficiently by enabling agents to learn, over some simulations, which messages are really useful and which ones are not With this information, agents will take more enlightened decisions concerning the messages they send and the ones they listen to By doing so, their coordination can be improved because the communication is more efficient; thus, the most important messages for the coordination have less chance to be lost The second approach consist of learning the best way to manage a disaster depending on which sectors of the city are in trouble This improves the coordination because agents have a plan telling each one of them the more important things to if there is a problem in a specific sector of the city The last approach presented in this short paper consist of enabling agents to anticipate their actions and other agents’ actions For this purpose, agents have to learn how the disaster evolves in time and how the agents’ actions interact with the environment With a better anticipation of the other agents’ actions, each individual agent will be able to construct more accurate long-term plans, which plans will help to improve the coordination of their actions because each agent will have an idea about what the other agents are doing Conclusion In conclusion, the RoboCupRescue simulation is a good testbed for the study of learning approaches used to improve agents’ coordination in a complex real-time environment It’s an ongoing research project at our laboratory to design and test some learning algorithms that will be well suited and useful for this type of complex real-time systems References [1] Boutilier Craig (1996) Planning, Learning and Coordination in Multiagent Decision Processes In Proceedings of TARK-96, De Zeeuwse Stromen, Hollande 627 [2] Kitano Hiroaki (2000) RoboCup Rescue: A Grand Challenge for Multi-Agent Systems In Proceedings of ICMAS 2000, Boston, MA 627 [3] RoboCupRescue Official Web Page http://www.r.cs.kobe-u.ac.jp/robocuprescue/ 627 Accent Classification Using Support Vector Machine and Hidden Markov Model Hong Tang and Ali A Ghorbani Faculty of Computer Science, University of New Brunswick Fredericton, NB, E3B 5A3, Canada {p518x, ghorbani}@unb.ca Abstract Accent classification technologies directly influence the performance of speech recognition Currently, two models are used for accent detection namely: Hidden Markov Model (HMM) and Artificial Neural Networks (ANN) However, both models have some drawbacks of their own In this paper, we use Support Vector Machine (SVM) to detect different speakers’ accents To examine the performance of SVM, Hidden Markov Model is used to classify the same problem set Simulation results show that SVM can effectively classify different accents Its performance is found to be very similar to that of HMM Introduction Accent is one of the most important characteristics of speakers Recently, accent detection became more focused and a number of researchers have published works not only on the features of foreign accent but also on accent identification Levent and Hansen used Hidden Markov Model (HMM) codebooks based on the acoustic features to identify three accents (American, Turkish, Chinese) affecting English[1] Currently, extensive researches are being carried out to find a suitable method that can effectively detect speakers’ accents There are two main factors – acoustic features and the classification models This paper proposes another classification model-Support Vector Machine (SVM) to detect the accents Support Vector Machine Besides HMM, ANN is the most popular model used to detect accents Unfortunately, ANN suffers from number of limitations such as overfitting, fixed topology and slow convergence Statistical learning techniques based on risk minimization such as Support Vector Machine (SVM) are found to be very powerful classification schemes Compared with ANN, SVM has several merits: (1) Structural Risk Minimization techniques minimize a risk upper bound on the VC-dimension, (2) among all hyperplanes separating the data, SVM can find a unique hyperplane that maximizes the margin of separation between the classes and (3) the power of SVM lies in using kernel function to transform data from the low dimension space to the high dimension space and construct a linear binary classifier Y Xiang and B Chaib-draa (Eds.): AI 2003, LNAI 2671, pp 629–631, 2003 c Springer-Verlag Berlin Heidelberg 2003 630 Hong Tang and Ali A Ghorbani In general, SVM is a binary classifier Recently, researchers have expanded the basic SVM to the multi-class SVM Such multi-class SVM has been successfully applied to different kinds of classification problems In our experiments, we use Pairwise SVM and DAGSVM to classify three accents – Canadian, Chinese and Indian accents Pairwise Multi-class SVM is also called 1-to-rest algorithm DAG multi-class method is to algorithm, which uses a Directed Acyclic Graph (DAG) to construct a binary tree 3.1 Implementation Speech Signal Database The speech database consists of 60 male speakers speech signals (20 Chinese speakers, 20 Canadian speakers and 20 Indian speakers).The choice of collecting speech data of one gender type (i.e male speakers) reduces the influence of the different pitch frequency that exists between males and females 3.2 Feature Extraction Foreign accent is a pronunciation feature of non-native speakers.Particular speech background groups generally exhibit some common acoustic features We can identify different accent groups according to such features There are four features we used in our experiments (1) Word-final Stop Closure Duration: the duration of the silence between the lax stop and the full stop; (2) Word Duration: the time between the start and end of the speech signal; (3) Intonation: the intonation depends on the syntax, semantics, and phonemic structure of particular language; (4) F2-F3 contour: presents different tongue movements The latter is the most powerful feature to distinguish different accents 3.3 Experiment Results and Conclusions To examine the performance of the SVM, we used HMM to detect the same accents The test results are shown in Table From the results obtained, we found that: Pairwise SVM is not as good as DAGSVM; DAGSVM has almost the same performance as HMM in three accents database; the simulation results show that SVM and HMM have almost the same convergence speed Table Detection Rates for SVM and HMM Pairwise SVM DAGSVM HMM 81.2% 93.8% 93.8% Accent Classification Using Support Vector Machine 631 References [1] Levent Arslan, John H L Hansen Language Accent Classification in American English Univeristy of Colorado Boulder, Speech Communication, Vol 18(4), pp.353-367, July 1996 629 [2] Levent M Arslan, John H L Hansen A study of Temporal Features and Frequency Characteristic in American English Foreign Accent, Duck University, Robust Speech Processing Laboratory http://www.ee.duke.edu/Resarch/speech [3] John C Platt, Nello Cristianini, John Shawe-Taylor Large Margin DAGs for Multiclass Classification A Neural Network Based Approach to the Artificial Aging of Facial Images Jeff Taylor Department of Computer Science, The University of Western Ontario jtaylo38@csd.uwo.ca Introduction After a child has been missing for a number of years, a photograph of the child is of limited use to law enforcement officials and the general public In order for a picture to be of any use, it should be artificially aged to provide at least an estimate of what the child currently looks like This age progression can be done by a forensic artist using specialized computer software; however, this is a subjective process that depends greatly on the skill and knowledge of the artist involved Therefore, this research aims at developing a system that performs automated age progression on images of human faces, with special attention to the case of children in the range of five to fifteen years of age Aging is a complex process involving many factors that differ from individual to individual Rowland and Perrett [1] describe a process that can transform facial images along different “dimensions” such as age, ethnicity, gender, and so on Of all these transformations, aging is the only one that occurs naturally This research intends to determine if the aging process can be learned by developing a neural network based system to perform artificial aging of images of human faces Neural networks have been chosen to implement this system because of their ability to model complex, nonlinear relationships, and because of their applicability to problems involving pattern recognition This research will be done in three main phases: data collection, design and training of the neural network, and testing of the neural network Data Collection In order to train the neural network, a large number of images will need to be collected, with the following restrictions: at least two images of each individual must be collected, and the age of the individual in each image must be known These restrictions will require a certain amount of selectivity in data collection Images cannot be taken from magazines or the Internet, for example, unless they meet the aforementioned restrictions Therefore, it will be necessary to collect pictures from people who are willing to submit pairs of photographs of themselves MSc student Y Xiang and B Chaib-draa (Eds.): AI 2003, LNAI 2671, pp 632–634, 2003 c Springer-Verlag Berlin Heidelberg 2003 A Neural Network Based Approach to the Artificial Aging of Facial Images 633 Design and Training The design of the neural network will need to be done with great care Neural network design is largely a subjective, trial-and-error process The complexity and unpredictability of the aging process mean that a suitable architecture may be difficult to obtain In order to cut down on the complexity and training time of the neural network, the data will have to be preprocessed before it can be used in the system Using individual pixels as inputs would lead to prohibitive training times; therefore, a number of feature points will be defined to describe each face Hwang et al [2] describe a method for face reconstruction using a small number of feature points; the feature points defined here can also be used for this research After these feature points have been located, each face will have to be normalized to a standard position and size A method for face normalization using the location of the eye strip is described by Gutta et al [3] While designing the neural network, the number and type of inputs will also need to be defined The inputs to the system will consist of the coordinates of each feature point in the source image, the age, gender, and ethnicity of the source image, and the age of the target image We propose to use a modified version of the Backprop algorithm, such as Quickprop or Cascade-Correlation, to train the neural network Once the neural network has been trained, its purpose will be to age input images artificially A user need only enter values for the inputs defined above Based on the values of these inputs, the system will determine the new locations of the feature points, map the new feature points onto the input image, and warp the input image so that it matches the new feature points Testing The third and final phase of research will be the testing of the system As the system is being trained, a number of pairs of images will be withheld and not used in the training of the system This set of images will be used strictly to test the system From each pair of images in the test set, the younger image will be input into the system The output of the system can then be tested in two ways: objectively and subjectively An objective test will measure how closely the feature points in the output image match those of the true older image A subjective test can be performed by visually inspecting the output images and seeing how closely they resemble the subjects 634 Jeff Taylor References [1] Rowland, D., Perrett, D.: Manipulating Facial Appearance through Shape and Color IEEE Computer Graphics and Applications 15 (1995), 70–76 632 [2] Hwang, B., Volker, B., Vetter, T., Lee, S.: Face Reconstruction from a Small Number of Feature Points International Conference on Pattern Recognition (2000), 842–845 633 [3] Gutta, S., Huang, J., Takacs, B., Wechsler, H.: Face Recognition Using Ensembles of Networks International Conference on Pattern Recognition (1996), 50–54 633 Adaptive Negotiation for Agent Based Distributed Manufacturing Scheduling Chun Wang1, Weiming Shen2, and Hamada Ghenniwa1 1 Dept of Electrical & Computer Engineering, The University of Western Ontario London, Ontario, Canada, N6G 1H1 cwang28@uwo.ca hghenniwa@eng.uwo.ca Integrated Manufacturing Technologies Institute, National Research Council Canada London, Ontario, Canada, N6G 4X8 weiming.shen@nrc.ca Extended Abstract Manufacturing scheduling problem is typically NP-hard While traditional heuristic search based approaches have been considered not suitable for dynamic environments because of their inherent centralized nature, agent based approaches are promising for their decentralized, autonomous, coordinated, and rational natures However, many challenging issues still need to be addressed when applying agent based approaches to complex distributed manufacturing scheduling environments One of them, namely adaptive negotiation [2], is to integrate intelligence and rationality into negotiation mechanisms and make the system more adaptive in dynamic environments This is very important to the manufacturing scheduling problem because the agent based scheduling process is essentially a coordination process among agents Adaptive negotiation is a way to achieve the coordination in a dynamic scheduling environment By adaptive negotiation we mean that more intelligence and rationality are integrated into the negotiation mechanism, thus make it adaptive to the changes of the dynamic scheduling environment To achieve this, issues at three levels have to be addressed: system architecture, agent architecture, and heuristics At the system architecture level, the system must have the architecture with corresponding characteristics to support adaptive negotiation among agents At the agent architecture level, an agent must have rational abilities (decision making mechanisms) embedded in the architecture to transfer the knowledge (in our case, negotiation heuristics) and environment conditions into specific negotiation behaviors The third level is the heuristics level By this we mean the knowledge that needs to be integrated into the agent negotiation mechanism and can be used by agents in terms of rational decision making At the system architecture level, we propose a hybrid architecture that is suitable for both inter-enterprise and intra-enterprise manufacturing scheduling environments The intra-enterprise environment consists of part agents, resource agents, a directory facilitator and a coordination agent These agents work in a cooperative distributed environment, thus have the same goal They communicate through an intranet behind a Y Xiang and B Chaib-draa (Eds.): AI 2003, LNAI 2671, pp 635-637, 2003 Springer-Verlag Berlin Heidelberg 2003 636 Chun Wang et al firewall The inter-enterprise environment consists of coordination agents from different enterprises They negotiate in a self-interested multi-agent environment, and communicate through the Internet The reasons that this architecture supports adaptive negotiation include: (i) this is a highly distributed architecture, no any agent in the system has the power over other agents; and each agent deals with dynamic environment autonomously, which increases the system responsiveness; (ii) the architecture integrates two kinds of manufacturing environments using coordination agents, which provides an infrastructure for enterprises to transfer the negotiation topics across the enterprise boundary, therefore, enhances the adaptive negotiation to the interenterprise level; (iii) the use of directory facilitator agents provides an easy way to update agents in the environment with up to date knowledge, which facilitates the adaptive nature of negotiation as well At the agent architecture level, we use the CIR (Coordinated Intelligent Rational) agent model [1] as our agent architecture In the CIR model, an agent is viewed as a composition of knowledge and capability; the capability, in turn, consists of problem solver, interaction and communication The CIR agent architecture accommodates adaptive negotiation in three aspects: (i) it focuses on the coordination which the adaptive negotiation has to achieve; (ii) interaction devices [1] are well classified and easy to be implemented with software components, which facilitates the selection of negotiation mechanisms in the context of adaptive negotiation; (iii) the decision making mechanism provided in the CIR agent architecture makes it easy for systems developers to design adaptive negotiation mechanisms Negotiation heuristics are the knowledge that an agent uses to make negotiation decisions under various situations Our approach regarding this aspect focuses on negotiation heuristics from economics We investigate the natures of different negotiation models used in economics; distinguish the important characteristics of different negotiation situations; and use a case based reasoning mechanism to match the different negotiation models to various situations A prototype environment for intra-enterprise manufacturing scheduling has been implemented at the National Research Council Canada’s Integrated Manufacturing Technologies Institute We are developing a new prototype that extends the current prototype into the inter-enterprise level by incorporating economically inspired negotiation mechanisms in the coordination agent of each enterprise, and integrating more adaptive negotiation mechanisms into the negotiation framework Currently, the requirement specifications, system analysis and part of the detailed system design have been done for the new prototype The detailed design and implementation of a software prototype are to be completed in Spring 2003 In summary, we address adaptive negotiation issues at three levels: system architecture, agent architecture, and heuristics Although the proposed approach seems to be complex, it has a very good potential to solving complex real world manufacturing scheduling problems as well as other complex resource management problems in dynamic environments Adaptive Negotiation for Agent Based Distributed Manufacturing Scheduling 637 References [1] [2] Ghenniwa, H and Kamel, M, Interaction Devices for Coordinating Cooperative Distributed Systems, Automation and Soft Computing, 6(2), 173-184, 2000 Shen, W., Li, Y., Ghenniwa, H and Wang, C Adaptive Negotiation for AgentBased Grid Computing, Proceedings of AAMAS2002 Workshop on Agentcities: Challenges in Open Agent Environments, Bologna, Italy, July 2002, pp 32-36 Multi-agent System Architecture for Tracking Moving Objects Yingge Wang and Elhadi Shakshuki Computer Science Department Acadia University Nova Scotia, Canada B4P 2R6 050244w; elhadi.shakshuki@acadiau.ca Abstract There is an increasing demand for both tools and techniques that track the location of people or objects This paper presents a multi-agent tracking system architecture that consists of several tracking software agents and uses GPS receivers as a signal-sending platform The system acts as a mediator between the user and the tracking object environment Objects carry Global Positioning System (GPS) receivers to locate their positions These objects can move around within a predefined area, which is divided into several sub-areas Each sub-area is monitored by one of the tracking agents All agents of the system have similar architecture and functions and are able to communicate and coordinate their activities with each other to trade information about the position of the object A prototype of this environment is being implemented to demonstrate its feasibility, using the ZEUS toolkit Introduction Many distributed resource-tracking problems exhibit a high degree of uncertainty due to the object’s movement, which makes it not easy to solve Binding a signal-sending hardware onto a targeted object is the most common solution for detecting the target However, the dynamism of the object’s movement makes it a difficult task to detect the moving object’s position in real time Agent-based technology is makes it possible to build a multi-agent system that can act as a mediator between the user and the moving object for tracking problems [1] Our proposed system uses GPS receiver to determine the precise longitude, latitude and altitude of an object [2] This location information changes as the object moves, so that the moving path can be traced Agents gather real-time position data from GPS receivers on each object At anytime, one tracking agent monitors the object When the object moves to a common area that is shared by more than one tracking agent, the agents need to communicate and coordinate which one will track the object System Architecture and Implementation In the object-tracking environment, the area in question is covered by identical circles, each with inner and outer rings that have the same centre points The inner-circles are tangential with each other, and the outer-circles are large enough to envelope the common area surrounded by the innercircles A tracking agent resides in the centre of each circle at a predefined position The agents continuously gather location information from GPS receivers and update their knowledge by sending/receiving information with other agents The agents engage in negotiation with each other when the object moves to the area between the inner-circle and outer-circle The architecture of the agent consists of a set of modules, as shown in Fig 1-a In this application, the agent’s knowledge includes the other agents’ model and the object model The other agents’ model comprises information about the user (in terms of his/her requests), and other tracking agents (in terms of their names, addresses and the area under their control) An agent builds its knowledge from the information received from GPS receivers as well as from the information received from other agents The agents utilize the information received from the GPS receivers, along with their own known position and predefined cover area, to determine whether the object is within their area and build their knowledge accordingly The agents also generate Y Xiang and B Chaib-draa (Eds.): AI 2003, LNAI 2671, pp 638-639, 2003 c Springer-Verlag Berlin Heidelberg 2003 Multi-agent System Architecture for Tracking Moving Objects 639 tracking history information, which is stored in a local database Agents communicate with each other using Knowledge Query Manipulation Language (KQML) [3] The user interacts with the system through a graphical user interface, as shown in Fig 1-b This interface also provides complete viewable information regarding the moving objects The Task Manager is a key component within a tracking agent It controls a tracking agent’s task in its sub-area and decides when to ask other agents for help In addition, it checks an object’s information by querying the Tracking Records Database This module makes the agent capable of extrapolating and reasoning its knowledge, and then come up with the solution for the desired tracking task The Communication Module allows the agent to exchange messages with other elements in the environment, including humans and other tracking agents When a tracking agent needs help, it sends out a request to all other agents in the system As soon as the other agents receive the request, they reply with answers to the sender agent within a pre-defined time frame Through this process, they can decide who will track the object The Database Manager is designed to record each object’s information in the Tracking Records Database for review During this process, the tracking agent keeps track of the object and also calculates the distance between the object and the location of the agent itself (a) (b) Fig (a) Tracking Agent Architecture and (b) An example of a user interface A prototype of the proposed system has been implemented using Java and the ZEUS toolkit [4] Each tracking agent acts as a publisher and when it is sending information and as a subscriber when it is receiving information The tracking agent action, which furthers the negotiation, is the sending of a FIPA-ACL message The only instance of waiting in a tracking agent’s negotiation is that of waiting for the reply message Three agents are created, namely: A, B and C All agents use a common ontology as defined by ZEUS toolkit The responsibilities of each agent that act as a publisher includes sending information to subscribers, responding to information received from subscribers, and performing its application-specific activities All messages and rules are represented in the format of the FIPA Communicative Act Library Specification To simplify the implementation, all tracking agents and objects are put at the same horizontal level References [1] H.S Nwana: “Software agents: an overview”, The Knowledge Engineering Review, 11(3), 1996 [2] GARMIN Corporation, “GPS Guide”, 2000 [3] Finin, T., Labrou, Y and Mayfield, J., ‘‘KQML as an Agent Communication Language’’, In Bradshaw J.M (Ed.) Software Agents, Cambridge, MA: AAA/MIT Press, pp 291-316, 1997 [4] J C Collis, D T Ndumu, H S Nwana and L C Lee, “The ZEUS agent building tool-kit” BT Technology J, 16(3), 1998 Author Index Abu-Draz, S 611 An, Aijun 206, 237 Anthony, Laurence 492 Baldi, Pierre Baldwin, Richard A Belacel, Nabil 616 Bento, Carlos 537 Bidyuk, Bozhena 297 Bot´ıa, Juan A 466 Brugman, Arnd O .596 Butz, Cory J 568, 583 Carreiro, Paulo 537 Cercone, Nick 237 Chaib-draa, B 353 Chatpatanasiri, Ratthachat 313 Chaudhari, Narendra S .515 Cohen, Robin 434 Coleman, Ron 472 Costa, Luis E Da 614 Dechter, Rina 297 Dijk, Elisabeth M A G van 596 Elazmeh, William 479 Elio, Ren´ee 50, 383 Ferreira, Jos´e Lu´ıs 537 Fink, Eugene 603 Frasson, Claude 563 Frost, Richard 66 Ghenniwa, Hamada 635 Ghorbani, Ali A 616, 629 Gomes, Paulo 537 G´ omez-Skarmeta, Antonio 466 Goodwin, Scott D 114, 618 Guan, Yu 616 Guillemette, Louis-Julien 24 Hamilton, Howard J 175, 486 Hershberger, John 603 Hoos, Holger H 96, 129, 145, 400, 418 Horsch, Michael C 160 Huang, Jin 329 Huang, Mingyan 618 Huang, Xiangji 237 Janzen, Michael 575 Japkowicz, Nathalie 222 Jarmasz, Mario 544 Jia, Keping 252 Jiang, Linhui 486, 621 Johnson, Josh 603 Kaltenbach, Marc 563 Karimi, Kamran 175, 624 Kemke, Christel 458 Kijsirikul, Boonserm 313 Kosseim, Leila 24 Kuipers, Jorrit 596 Kusalik, Anthony J 520 Labrie, M A 353 Landry, Jacques-Andr´e 614 Lashkia, George V 492 Lesser, Victor Ling, Charles X 329, 591 Lingras, Pawan 557 Liu, Zhiyong 618 Lo, Man Hon 81 Lu, Fletcher 342 Maguire, Robert Brien 527 Marco, Chrysanne Di 550 Matwin, Stan 222, 498 Maudet, N 353 McCracken, Peter 190 McNaughton, Matthew 35 Mellouli, Sehl 370 Mercer, Robert E 550 Messaouda, Ouerd 498 Milios, Evangelos 268, 283 Mineau, Guy W 370, 505 Mitchell, Tom M Moulin, Bernard 370 642 Author Index Neufeld, Eric Nijholt, Anton 596 Oommen, John B .498 Paiva, Paulo 537 Pang, Wanlin 114 Paquet, S´ebastien 627 Pavlin, Michael 96 Pelletier, Francis Jeffry 50 Peng, Fuchun 237 Pereira, Francisco C 537 Petrinjak, Anita 383 Plamondon, Luc 24 Razek, Mohammed Abdel 563 Redford, James 35 Ruiz, Pedro 466 Salort, Jose 466 Schaeffer, Jonathan 35 Schuurmans, Dale 237, 342 Seco, Nuno 537 Shakshuki, Elhadi 611, 638 Shen, Weiming 635 Shi, Zhongmin 268 Shmygelska, Alena 400 Silver, Daniel L 190 Smyth, Kevin 129 Soucy, Pascal 505 Spencer, Bruce 252 Stă utzle, Thomas 96, 129 Szafron, Duane 35 Szeto, Kwok Yip 81 Szpakowicz, Stan 544 Tang, Hong 629 Taylor, Jeff 632 Tompkins, Dave A D 145 Tran, Thomas 434 Tsvetinov, Petco E 447 Tulpan, Dan C 418 Upal, M Afzal 510 Wan, Qian 206 Wang, Chun 635 Wang, Yingge 638 Weevers, Ivo 596 West, Chad 557 Wong, S K Michael 568, 583 Wu, Dan 568, 583 Wu, Fang-Xiang 520 Xiang, Yang 575 Xiangrui, Wang 515 Yan, Rui 557 Yang, Simon X 532 Yao, Yiyu Yao 527 Yu, Xiang 532 Zaluski, Marvin 222 Zhang, Harry 329, 591 Zhang, W J 520 Zhang, Yiquing Zhang 283 Zhao, Yan 527 Zheng, Jingfang 160 Zincir-Heywood, Nur 268, 283 Zwiers, Job 596 ... Brahim Chaib-draa (Eds.) Advances in Artificial Intelligence 16th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2003 Halifax, Canada, June 1 1-1 3, 2003 Proceedings... BertelsmannSpringer Science+Business Media GmbH http://www.springer.de © Springer-Verlag Berlin Heidelberg 2003 Printed in Germany Typesetting: Camera-ready by author, data conversion by DA-TeX Gerd... and B Chaib-draa (Eds.): AI 2003, LNAI 2671, pp 9-2 3, 2003 Springer-Verlag Berlin Heidelberg 2003 10 Richard A Baldwin and Eric Neufeld Wright [10, pp 177 5-7 6] divides cases of overdetermined