This minitrack aims at being a premier presentation forum for the latest ideas and results in the area of agent-based adaptive simulation. We seek research papers, case studies and practitioner reports relating to agent-based simulation methods, environments, and methodologies.

Relevant topics for this minitrack include (but are not limited to):

• Business Applications And Case Studies
• Architectures And Environments
• Standards
• Experimental Studies
• Large Scale Simulations
• Web-Based Simulations
• Model Verification, Validation And Calibration
• Solution Comparisons
• Integration With Decision Models
• Constructive And Destructive Agents
• Artificial Organizations, Societies And Economies

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Data Mining and Knowledge Discovery

Comprehensibility of data mining techniques, data visualization, task and model interaction, data quality assessment, interpretability, scalability, human factors and modeling, performance measurement and validation, acquisition of qualitative knowledge, feature selection, and the economics of decisions are some of the topics being sought. However, the topical areas will not limited to the above. Relevant software and economic issues will also be considered.

Possible topics include:

Measuring Model Performance Task Characteristics Multimedia and Temporal Data Knowledge re-use Human Factors and Environmental Issues Mining Methodology Issues Model Interpretation Economics and Cost of Errors Data Visualization

Algorithms and Tools Data Quality, Size, and Representation Qualitative Knowledge Active Learning Scalability and Preprocessing Sensitivity and Generalizability Geographic Data Application Case Studies Industry Projects

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Intelligent Systems And Soft Computing

This mini-track is focused on the theory and applications of intelligent systems and soft computing in management.

This includes (but is not limited to) processes of problem solving, planning decision making, in contexts which range from strategic management, business process reengineering and electronic commerce, to production, marketing and financial management, and to smarter IS applications for operational management.

The methodologies used may be analysis or systems oriented, they may be actions research or case based, or they may be experimentally or empirically focused. Studies are favoured, which combine good theoretical results with careful empirical verifications, or good empirical problem solving with innovative theory building. A common denominator for all studies should be the design and use of intelligent and/or soft computing systems.

Soft computing includes research on fuzzy logic, artificial neural nets, genetic algorithms and probabilistic modelling.

Fuzzy sets were introduced by Zadeh as a means of representing and working with data that was neither precise nor complete, but vague and incomplete. Fuzzy logic provides an inference morphology, which enables the principles of approximate human reasoning capabilities to be systematically used as a basis for knowledge-based systems.

The theory of fuzzy logic provides a good mathematical and methodological basis for capturing the uncertainties associated with human cognitive processes, such as identifying causal relationships, thinking and reasoning. The conventional approaches to knowledge representation lack the means for representing the meaning of vague and incompletely understood concepts. As a consequence, the approaches based on first order logic and classical probability theory do not provide appropriate conceptual frameworks for dealing with the complexities of real world problems and common sense knowledge, since such knowledge is by its nature lexically imprecise, non-categorical and incomplete.

The problems outlined with the standard, conventional representations of knowledge are well known for everybody working with support systems, both when designing and building the systems, and when trying to implement them and to make them work for real world applications. When used to deal with the complexities of real world applications - especially when they are designed to deal with management problems - systems constructs have become large and complex, quite hard to understand and build, and even harder to use and support. Clearly, there is a need for alternative approaches, and knowledge based systems built with fuzzy logic have started to appear as viable alternatives.

The development of fuzzy logic was motivated - to a large extent - by the need for a conceptual framework which can address the issues of uncertainty, lexical imprecision and incompleteness. Some of the important characteristics of fuzzy logic include:

• exact reasoning is viewed as a limiting case of approximate reasoning;
• knowledge is interpreted as a collection of elastic or fuzzy constraints on a collection of variables;
• inference is viewed as a process of propagation of elastic constraints;
• any logical system has a fuzzy logic based counterpart;

There are two main characteristics of fuzzy systems that give them better performance for specific applications:

1. fuzzy systems are suitable for uncertain or approximate reasoning, especially when the systems are difficult to describe with a mathematical model;
2. fuzzy logic allows problem solving and decision making with estimated values on the basis of incomplete or uncertain information.

Intelligent systems include the following categories of systems:

• Knowledge based systems
• Expert systems
• Software agents
• Multiple criteria optimisation and support systems
• Genetic algorithms
• Artificial neural nets and self-organising maps
• Innovative and active DSS
• Smart MS Office & Windows designs

Intelligent support systems should help managers and knowledge workers to more intuitive and effective use of knowledge and information in problem solving, planning and decision making, and should help to build innovative and creative support for operations and management.

Multiple criteria optimisation and support systems help to find the best possible solutions for well-structured problems, and innovative and active DSS provide interactive, intelligent tools for handling semi- and ill-structured problems.

We can have intelligent user interfaces for both types of support systems in order to enhance the productivity of the working time spent with the systems. As the use of MS Office and Windows environments keeps growing, poor application designs, and a less than optimal use of the best features of the software, have created productivity problems for the systems users. There is a need for smart systems designs with a track record of actually improving the productivity of systems users.

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Intelligent Systems in Traffic and Transportation

The minitrack focuses on Intelligent Systems which are able to assist the design-phase (strategic planning) of traffic and transportation systems and/or the management-phase (tactical and operational planning) as well. The purpose of the transportation logistics is to design, to organize and to manage transportation in order to meet customer service demands and cost and environmental requirements. Such logistics systems must comply with regulations on traffic, laws on labor and other types of constraints. In the field of transportation logistics we will focus on the analysis of urban, regional and intercity transportation networks for both passenger and freight transportation as well. Complex hybrid-type systems which include air-, road- and rail transportation as well are of particular interest.

Since the beginning of the last century an extraordinary development of transport demand is evident. This is a result of industrialisation and the supply of new transport modes which at last made substantial changes of economy possible. The growing standard of living changed living and behaviour patterns; faster and cheaper transport modes gave the impulse to see other regions, doing business with partners living farther away, who are able to offer goods cheaper than in the own region.

Therefore the increasing labour distribution in economy, the concentration of population in agglomerations with simultaneous migrations from rural regions, the demand for recreation for man being daily stressed in professional life as well as the improvement of rail, car, and air transport supply can be regarded as the principle causes for the enormous increase of demand.

To satisfy the increased demand for movement, to be economical with the use of public resources whilst encouraging economic growth, requires careful attention to monitoring trends and forecasting. Moreover, growth itself carries dangers. The expansion of transport facilities is not cost-free in terms of the environment and social welfare. A balance has therefore to be struck that satisfies a number of possibly conflicting goals. Such a balance requires that there are models forecasting the traffic demand and the changes in modal split and travel behaviour.

Intelligent Systems which are designed to solve real world applications in traffic and transportation are built on the basis of an advanced software engineering concept including object-oriented software development and integration with non-standard databases and GIS. On the algorithmic side several so-called Intelligent Techniques coming from the AI, the OR and the CI, such as Tabu Search Metaheuristics, Evolutionary and Genetic Algorithms, Constraint Programming, but also high performance Optimization or Simulation techniques are used.

We seek research papers, case studies and practitioners reports relating to the design, the implementation and the use of Intelligent Systems built for particular problems in Traffic and Transportation. The papers need not to present fully developed complex systems. Conceptual papers, empirical papers and papers dealing with particular components of such a system, e.g. the Intelligent Techniques, are also welcome.

Therefore, relevant topics for the minitrack include (but are not limited to)

1. Modeling Intelligent Systems in Traffic and Transportation
   • Models for the estimation of future volume of traffic likely to be affected by planned projects or management policy
   • Models introducing changes in travel behaviour
   • Modal split models
   • Transportation Network Design Problems including different modes of transportation and hubs
   • Vehicle Routing and Crew Scheduling Problems (e.g. in air transportation)
   • Dynamic Vehicle Routing and Dispatching
2. Intelligent Techniques applied to combinatorial optimization problems in traffic and transportation logistics
   • Tabu Search Metaheuristics
   • Population-based methods (Genetic and Evolutionary Algorithms)
   • Constraint programming
   • Hybrid methods
3. Conceptual papers on projects and reports on Intelligent Systems in practical use

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Management of Telecommunication Networks and Distributed Systems

Moreover, the increasing decentralization of network services as exemplified by the growing importance of workstations and client-server computing makes coherent and coordinated network management even more difficult. Multi vendor environment as well as multimedia information flow and requirements further complicate the problem.

Both wired and wireless networks are of interest. Examples of networks/technologies include ATM, fast and gigabit Ethernet, SONET-based fiber optic networks, Internet, cellular and PCS networks, wireless local loops, wireless LANs, satellite-based networks, wireless WANs, mobile Internet Protocol, and wireless ATM.

Applying either quantitative techniques including simulation, emerging technology solutions, or business/economic models to control network traffic, the minitrack discusses the different approaches to enhance the network performance and business benefits of the distributed information system. Capacity planning, performance modeling, system administration, and enterprise network management are covered. While the papers emphasize the management and control aspect, the topical areas will not limited to the above. Relevant software, engineering, and industry issues will also be considered.

Possible Topics may include the following:

• Emerging network management technologies
• Network management model
• Distributed system architecture
• Design and modeling of networks
• Intelligent networks
• Network management tools
• Network performance monitoring and capacity planning
• Network security and control
• Interconnection of wireless and wired networks
• Integration of systems and applications
• Strategies and pricing of network management service

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Modeling Knowledge-Intensive Processes: Concepts, Methods, And Applications

The objective of this minitrack is to provide a forum for emerging research on the modeling and use of process knowledge. In the past, much of research in this area has focused on tools and techniques for the capture and use of design rationale for software. However, researchers are increasingly focusing on multiple facets of the problem, e.g., capturing and retaining implicit knowledge or devising organizational incentives for designers to create and use process knowledge. It is our contention that widespread use of knowledge intensive processes in organizations requires integration among the diverse aspects of the problem. This can be accomplished by providing mechanisms through which researchers can exchange perspectives on different aspects of the problem. This minitrack is intended to be such a forum.

Focus of Minitrack

Our objective is to encourage submissions on multiple aspects of the problem as well as promote diversity in perspectives. Accordingly, the scope of the minitrack will encompass research on modeling concepts, methods, and applications. We also welcome submissions that focus on the use and efficacy of process knowledge in the design of products, systems or services.

Relevant topics for this minitrack include (but are not limited to) the following:

1. Modeling Process Knowledge: Concepts and Methods
   • Concepts and methods for the capture and use of process knowledge in design tasks.
   • Capturing and retaining "embedded" or implicit knowledge.
   • Models and methods for linking process knowledge emerging from a particular design situation with the knowledgebase and memory of the larger organization.
   • Factors influencing (impeding and facilitating) comprehensive capture and use of process knowledge.
   • Frameworks for the evaluation of methods and tools.
   • Tangible and intangible costs and benefits of managing process knowledge.
2. Modeling Process Knowledge: Applications
   • Innovative implementations of "proof-of-concept" systems to support knowledge intensive processes.
   • Systems for supporting virtual design teams.
   • Case studies or experience reports on the capture and use of process knowledge in organizations.
   • Empirical research on the use and efficacy of process knowledge in design and maintenance of products/systems.
   • Systems to support rapid and evolutionary development.
   • Traceability of process knowledge across system components and phases.
   • Intelligent tools for management of process knowledge.
   • Systems to support concurrent development.
   • Process knowledge in formal software development.

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