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2005 Graduate Catalog and 2004 Annual R & D Report Sirindhorn International Institute of Technology (SIIT) Reasoning with UML Diagrams The Unified Modeling Language (UML) is a graphical language, adopted as a standard by the Object Management Group (OMG), for visualizing, specifying, constructing, and documenting the artifacts of a software-intensive system. As reported by recent works on the formal semantics of UML, there exist inherent interrelationships between components of a UML model. Such interrelationships constitute part of general knowledge about the domain of UML, which may be used, e.g., for deriving implicit properties and verifying the consistency of the model. A framework for knowledge representation and reasoning in the domain of UML is proposed, in which a UML model is represented as textual XML data, and the general knowledge about the UML domain as an XML declarative description. Development of an inference engine for automatic refinement of the encoded UML diagrams and derivation of implicit model properties is underway. Equivalent-Transformation Computation Model In declarative paradigms, a declarative description plays the role of a precise specification, and, at the same time, operates as a program. A number of works on amalgamation and generalization of declarative languages have been proposed. Most of them have been driven mainly by computationoriented requirements, e.g., enhancement of operational semantics and integration of computation models; other important related concepts such as program synthesis and program transformation are investigated only afterwards and not inherent in their designs. By contrast, the equivalent transformation (ET) paradigm takes a program-synthesis-oriented approach, i.e., effective generation of efficient and correct programs from specifications is its underlying design motivation. Theoretical investigation of the ET computation model is in progress. A Theoretical Framework for Comparing Computation Models Systematic generation of programs relies on some specific practically-determined correctness relations, which associate with each specification a number of cost-effectively-derivable correct programs with respect to it. Between two such correctness relations, if one is more expressive than the other, the former is preferable, i.e., it determines a larger space of derivable correct programs. Program generation in different computation paradigms, e.g., logic programming (LP), constraint logic programming (CLP), functional programming (FP), functional logic programming (FLP), and equivalent transformation (ET), employs different correctness relations, and the expressiveness thereof cannot be compared directly due to the discrepancy in the forms of specifications, programs and computations. The need arises for a means of comparing the expressiveness of correctness relations across computation frameworks. Dr. Junalux Chalidabhongse Assistant Professor B.Eng. in Computer Engineering, Chulalongkorn University, Thailand M.S. in Management Science, California State University at Fullerton, USA M.S. in Engineering-Economic Systems, Stanford University, CA, USA Ph.D. in Engineering-Economic Systems and Operations Research, Stanford University, CA, USA Areas of Specialization: Computational organizational modeling, Organizational learning, Organizational decision making, Decision support systems. Research Interest: Computational Organizational Modeling In order to examine how groups, organizations, and societies change over time, the ideas from organizational theory, sociology, psychology, management science, and intelligent systems have been combined to develop a computational model that can be used to represent organizational dynamics. By applying computational models of organizations, the behavior in organizations and the interaction among organizations can be studied and analyzed through the use of computer simulation. Research on computational organizational modeling is composed of two main components. First, it includes the study to advance understanding of problemsolving in human groups, ranging from small cooperative teams to large-scale, highly-distributed enterprises: organizational decision making, strategic business planning, and adaptive crisis response in organizations. Second, the research includes the development of computer-based technology to support organizational decision making and problem solving. The research focuses on the design, development, and evaluation of computer-based intelligent systems that aid organizational decisionmakers in coordinating their efforts to solve complex problems. 24
2005 Graduate Catalog and 2004 Annual R & D Report Sirindhorn International Institute of Technology (SIIT) Dr. Komwut Wipusitwarakun Assistant Professor (Half-time) B.S. (2 nd Class Honors) in Electrical Engineering, Chulalongkorn University, Thailand M.S. in Communication Engineering, Osaka University, Osaka, Japan Ph.D. in Communication Engineering, Osaka University, Osaka, Japan Areas of Specialization: Broadband communication networks, Network reliability analysis, Self-healing network design, ATM and TCP/IP technologies, Congestion control technologies. Research Interests: Highly Reliable Wide Area Network Design In the coming “Information Age”, business and daily life will be highly reliant on telecommunication services. All organizations, companies and ordinary homes will be connected together by the Wide Area Communication Networks (WAN) so that various kinds of services, provided at anywhere, can be accessible from everywhere at anytime. Network reliability will become a vital concern since the failure of network functionality will result in a significant impact on a wide-range of users both in tangible and intangible forms. Thus, technologies for designing and assuring the high reliability of WAN are needed. These include, for example, theory and tools to analyze the reliability-level of networks, automatic rerouting algorithms (self-healing algorithm) design, reliability-level based traffic prioritizing scheme, working and spare capacity design and plans to upgrade reliability-level of existing networks, etc. Virtual Private Network The Virtual Private Network (VPN) is technology to enhance the utilization of an unreliable connection traversing through public networks (either circuitswitching-based or IP(Internet Protocol)-based network) and sharing bandwidths with other users to create a reliable/secured connection (virtual private connection) like a conventional leased circuit, but with much lower costs. The IP-based VPN is promising since IP-based applications are widely used in all communities. The IP-based VPN technology involves designing a security scheme to protect transferred data from other users, a bandwidth management scheme to retain the acceptable bandwidth-level of the connection and a parallel data-transferring scheme to create a virtual high-bandwidth connection from a group of low-bandwidth connections. Dr. Matthew N. Dailey Lecturer B.Sc. & M.Sc. in Computer Science, North Carolina State University, USA Ph.D. in Computer Science and Cognitive Science, University of California, San Diego, USA Areas of Specialization: Machine Learning, Machine Vision, Robotics. Research Interests: Structure Learning for Autonomous Mobile Robots Intelligent systems and robotics technology together stand poised to revolutionize the way human beings live and work, thanks in large part to the increasing availability of enormous quantities of computing power at very low cost. But despite great advances in mechatronics, when compared to humans (or even rats), modern technology is deficient in the perception of the surrounding environment. Today's robots cannot see much better than the simplest insects. Machine vision research aims to close this crucial gap, but thus far, we have only rudimentary algorithms for inferring the 3D structure of the world from one or more moving cameras. New developments in statistical learning, however, have already transformed many areas of artificial intelligence, and promise to transform machine vision research in the same way. The general framework of my research is to 1) formulate 3D structure learning problems as problems of statistical inference, 2) specify statistical models appropriate for the problem at hand, and 3) devise efficient algorithms for inference under said statistical models. One example in my current work is Bayesian estimation of feature correspondences in trinocular stereo images. In stereo, the goal is to find corresponding features in two or more cameras (three cameras in the case of trinocular stereo) with known calibration parameters. Then, we use triangulation to determine the distance to the identified feature. Once a robot knows which points correspond to each other in a set of images, it can construct a 3D representation of the nearby environment. But finding these correspondences is a challenging problem, and current existing approaches are not accurate enough, robust enough, or efficient enough for real-time use by autonomous mobile robots. I believe that the statistical learning approach will lead to new, effective solutions to this and other difficult problems in robot visual perception. 25
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