A Machine Learning Feature Reduction Technique for Feature Based Knowledge Systems

Distributed Intelligence Systems: A New Paradigm for Systems Integration

(August 13, 2007, Monday)

Toward Human-Level Machine Intelligence

Lotfi A. Zadeh

Professor in the Graduate School, Computer Science Division
Department of Electrical Engineering and Computer Sciences
University of California, USA.

Director, Berkeley Initiative in Soft Computing (BISC)
zadeh@eecs.berkeley.edu

ABSTRACT

Achievement of human-level machine intelligence has profound implications for modern society—a society which is becoming increasingly infocentric in its quest for efficiency, convenience and enhancement of quality of life.

Humans have many remarkable capabilities. Among them a capability that stands out in importance is the human ability to perform a wide variety of physical and mental tasks without any measurements and any computations, based on perceptions of distance, speed, direction, intent, likelihood and other attributes of physical and mental objects. A familiar example is driving a car in city traffic. Mechanization of this ability is a challenging objective of machine intelligence.

Science deals not with reality but with models of reality. In large measure, models of reality in scientific theories are based on classical, Aristotelian, bivalent logic. The brilliant successes of science are visible to all. But when we take a closer look, what we see is that alongside the brilliant successes there are areas where achievement of human-level machine intelligence is still a distant objective. We cannot write programs that can summarize a book. We cannot automate driving a car in heavy city traffic. And we are far from being able to construct systems which can understand natural language.

Why is the achievement of human-level machine intelligence a distant objective? What is widely unrecognized is that one of the principal reasons is the fundamental conflict between the precision of bivalent logic and imprecision of the real world.

In the world of bivalent logic, every proposition is either true or false, with no shades of truth allowed. In the real world, as perceived by humans, most propositions are true to a degree. Humans have a remarkable capability to reason and make rational decisions in an environment of imprecision, uncertainty, incompleteness of information and partiality of truth. It is this capability that is beyond the reach of bivalent logic—a logic which is intolerant of imprecision and partial truth.

A much better fit to the real world is fuzzy logic. In fuzzy logic, everything is or is allowed to be graduated, that is, be a matter of degree or, equivalently, fuzzy. Furthermore, in fuzzy logic everything is or is allowed to be granulated, with a granule being a clump of elements drawn together by indistinguishability, similarity, proximity or functionality. Graduation and granulation play key roles in the ways in which humans deal with complexity and imprecision. In this connection, it should be noted that, in large measure, fuzzy logic is inspired by the ways in which humans deal with complexity, imprecision and partiality of truth. It is in this sense that fuzzy logic is human-centric.

In coming years, fuzzy logic and fuzzy-logic-based methods are likely to play increasingly important roles in achievement of human-level machine intelligence. In addition, soft computing is certain to grow in visibility and importance. Basically, soft computing is a coalition of methodologies which in one way or another are directed at the development of better models of reality, human reasoning, risk assessment and decision making. This is the primary motivation for soft computing—a coalition of fuzzy logic, neurocomputing, evolutionary computing, probabilistic computing and machine learning. The guiding principle of soft computing is that, in general, better results can be achieved through the use of constituent methodologies of soft computing in combination rather than in a stand-alone mode.

BIOGRAPHY

Lotfi Zadeh is a Professor in the Graduate School, Computer Science Division, Department of EECS, University of California, Berkeley. In addition, he is serving as the Director of BISC (Berkeley Initiative in Soft Computing).

Lotfi Zadeh is an alumnus of the University of Teheran, MIT and Columbia University. He held visiting appointments at the Institute for Advanced Study, Princeton, NJ; MIT, Cambridge, MA; IBM Research Laboratory, San Jose, CA; AI Center, SRI International, Menlo Park, CA; and the Center for the Study of Language and Information, Stanford University. His earlier work was concerned in the main with systems analysis, decision analysis and information systems. His current research is focused on fuzzy logic, computing with words and soft computing, which is a coalition of fuzzy logic, neurocomputing, evolutionary computing, probabilistic computing and parts of machine learning.

Lotfi Zadeh is a Fellow of the IEEE, AAAS, ACM, AAAI, and IFSA. He is a member of the National Academy of Engineering and a Foreign Member of the Russian Academy of Natural Sciences, the Finnish Academy of Sciences, the Polish Academy of Sciences, Korean Academy of Science & Technology and the Bulgarian Academy of Sciences. He is a recipient of the IEEE Education Medal, the IEEE Richard W. Hamming Medal, the IEEE Medal of Honor, the ASME Rufus Oldenburger Medal, the B. Bolzano Medal of the Czech Academy of Sciences, the Kampe de Feriet Medal, the AACC Richard E. Bellman Control Heritage Award, the Grigore Moisil Prize, the Honda Prize, the Okawa Prize, the AIM Information Science Award, the IEEE-SMC J. P. Wohl Career Achievement Award, the SOFT Scientific Contribution Memorial Award of the Japan Society for Fuzzy Theory, the IEEE Millennium Medal, the ACM 2001 Allen Newell Award, the Norbert Wiener Award of the IEEE Systems, Man and Cybernetics Society, Civitate Honoris Causa by Budapest Tech (BT) Polytechnical Institution, Budapest, Hungary, the V. Kaufmann Prize, International Association for Fuzzy-Set Management and Economy (SIGEF), the Nicolaus Copernicus Medal of the Polish Academy of Sciences, the J. Keith Brimacombe IPMM Award, the Silicon Valley Engineering Hall of Fame, the Heinz Nixdorf MuseumsForum Wall of Fame, other awards and twenty-six honorary doctorates. He has published extensively on a wide variety of subjects relating to the conception, design and analysis of information/intelligent systems, and is serving on the editorial boards of over sixty journals.
 

Mathematical Theory of Reusability 

Michael Leyton

Associate Professor,

Center for Discrete Mathematics & Theoretical Computer Science (DIMACS),
Rutgers University, USA

ABSTRACT

This talk gives an introduction to my book A Generative Theory of Shape (Springer-Verlag, 550pages). The purpose of the book is to develop a generative theory that has two properties regarded as fundamental to intelligence – maximizing reusability of structure and maximizing recoverability of the generative operations. These two properties are particularly important in the representation of complex organization – which is the main concern of the book. The primary goal of the theory is the conversion of complexity into understandability. For this purpose, a mathematical theory is presented of how understandability is created in a structure. This is achieved by developing a group-theoretic approach to formalizing reusability and recoverability. To handle highly complex structure, a new class of groups is invented, called unfolding groups. These unfold structure from a maximally collapsed version of that structure. A principal aspect of the theory is that it develops a new algebraic formalization of major object-oriented concepts such as inheritance. The consequence that the book establishes a representational language for complex organizational structure, that is interoperable by virtue of the principles on which the theory is based: reusability and recoverability.
The book gives extensive applications of the theory to CAD/CAM, human and machine vision, robotics, software engineering, and physics. For example, the theory is used to give new and detailed insights into the main stages of mechanical CAD/CAM: part-design, assembly and machining. And within part-design, an extensive analysis is given of sketching, alignment, dimensioning, resolution, editing, sweeping, feature-addition, and intent-management. In robotics, several levels of analysis are developed for manipulator structure and kinematics. In software, a new theory is given of the principal factors such as text and class structure, object creation cloning and modification, as well as inheritance and hierarchy prediction. In physics, a new theory is given of the conservation laws, and motion decomposition theorems in classical and quantum mechanics. In perception, extensive theories are developed for Gestalt grouping criteria, orientation and form, the prototype phenomena, and the main Gestalt motion phenomena (induced motion, separation of systems, the Johannson relative/absolute motion effects.

BIOGRAPHY

Michael Leyton's mathematical work on shape has been used by scientists in over 40 disciplines from aerospace engineering to radiology. His scientific contributions have received major prizes, such as a presidential award and a medal for scientific achievement. His new foundations to geometry are elaborated in his books in Springer-Verlag and MIT Press. Besides his scientific and mathematical work, he is also a highly exhibited painter and sculptor, and his architecture designs have been published by Birkhauser-Architectural. Also he is the composer of published string quartets. He is president of the International Society for Mathematical and Computational Aesthetics, and is an advisor to NSF on innovation in computer and information sciences and engineering. He is the keynote and invited speaker in conferences on virtually every scientific and artistic discipline. Currently, he is writing a 4-volume work on the foundations of science, with particular emphasis on quantum mechanics. He also continues to work on the structure of software, as well as interoperability and large-scale engineering systems integration, in the mechanical-aerospace industry. Professor Leyton is on the faculty of the DIMACS Center for Discrete Mathematics and Theoretical Computer Science, and the Psychology Department, at Rutgers.

A Machine Learning Feature Reduction Technique for Feature Based Knowledge System 

Daniel Yeung

Professor,

The Hong Kong Polytechnic University, Kowloon, Hong Kong

csdaniel@inet.polyu.edu.hk

ABSTRACT

Generalization error model provides a theoretical support for a pattern classifier's performance in terms of prediction accuracy. However, existing models give very loose error bounds. This explains why classification systems generally rely on experimental validation for their claims on prediction accuracy. In this talk we will revisit this problem and explore the idea of developing a new generalization error model based on the assumption that only prediction accuracy on unseen points in a neighbourhood of a training point will be considered, since it will be unreasonable to require a pattern classifier to accurately predict unseen points "far away" from training samples. The new error model makes use of the concept of sensitivity measure for a multiplayer feedforward neural network (Multilayer Perceptron or Radial Basis Function Neural Network). It could be demonstrated that any knowledgebase system represented by a set of features may be simplified by reducing its feature set using such a model. A number of experimental results using datasets such as the UCI and the 99 KDD Cup will be presented.

BIOGRAPHY

Daniel S. Yeung is the President-Elect of the IEEE Systems, Man and Cybernetics (SMC) Society, a Fellow of the IEEE and an IEEE Distinguished Lecturer. He received the Ph.D. degree in applied mathematics from Case Western Reserve University. In the past, he has worked as an Assistant Professor of Mathematics and Computer Science at Rochester Institute of Technology, as a Information Scientist in the General Electric Corporate Research Center, and as a System Integration Engineer at TRW, all in the United States. He was the chairman and professor of the department of Computing, The Hong Kong Polytechnic University, Hong Kong, from 1992 to 1999, and a Chair Professor of the same department from 1996 to 2006. Presently he is the President of the Machine Learning and Cybernetics Research Institute in Hong Kong. His current research interests include neural-network sensitivity analysis, data mining, Chinese computing, and fuzzy systems. He was the Chairman of IEEE Hong Kong Computer Chapter (91and 92), an associate editor for both IEEE Transactions on Neural Networks and IEEE Transactions on SMC (Part B). He is a member of the Board of Governor, a Vice President for Technical Activities, and currently a Vice President for Long Range Planning and Finance for the IEEE SMC Society. He co-founded and served as a General Co-Chair since 2002 for the International Conference on Machine Learning and Cybernetics held annually in China. He also served as a General Co-Chair (Technical Program) of the 2006 International Conference on Pattern Recognition. He is also the founding Chairman of the IEEE SMC Hong Kong Chapter.

Distributed Intelligence Systems: A New Paradigm for Systems Integration

William A. Gruver
President, Intelligent Robotics Corporation
Professor Emeritus, Simon Fraser University
w.gruver@ieee.org

ABSTRACT

Centralized systems have disadvantages that make them unsuitable for large-scale integration, including reliance on centralized communication, high complexity, lack of scalability and robustness, and high cost of integration. The use of distributed intelligence system technologies avoids these weaknesses. Distributed intelligence systems are based on the use of cooperative agents, organized in hardware or software components, that independently handle specialized tasks and cooperate to achieve system-level goals and achieve a high degree of flexibility. By distributing the logistic and strategic requirements of a system, it is possible to achieve greatly improved robustness, reliability, scalability, and security. Key to achieving these benefits is the use of holonic system technologies that establish a peer-to-peer environment to enable coordination, collaboration, and cooperation within the network. Such systems require both hardware and software components.

This lecture provides an overview of the technologies of distributed intelligence systems that have been developed by the presenter and his company, students, and colleagues, in cooperation with the Holonic Manufacturing Systems Consortium, and members of the Technical Committee on Distributed Intelligent Systems of the IEEE Systems, Man, and Cybernetics Society. Hardware and software architectures will be described for a wireless distributed intelligence system infrastructure being developed at Intelligent Robotics Corporation and the Intelligent/Distributed Enterprise Automation Laboratory of Simon Fraser University. Applications of distributed intelligence systems will be described, including distributed trading of digital services, RFID-based personnel and goods tracking, wireless automated meter reading, and resource management of distributed energy systems.
 

BIOGRAPHY

William A. Gruver is President of Intelligent Robotics Corporation and Professor Emeritus of Engineering Science at Simon Fraser University. He received the PhD, MSEE, and BSEE degrees from the University of Pennsylvania and the DIC in Automatic Control Systems from Imperial College of Science and Technology.

His industrial experience includes management and technical leadership positions at GE Factory Automation Products Division in Charlottesville; GE Industrial Automation Center in Frankfurt, Germany; IRT Corporation in San Diego, Center for Robotics and Manufacturing Systems at the University of Kentucky, and LTI Robotic Systems, a California based startup that he co-founded. He has also held engineering and faculty positions at the NASA Marshall Space Flight Center, DFVLR German Space Research Center, Technical University Darmstadt, U.S. Naval Academy, University of Kentucky, and North Carolina State University.

He has published 210 technical papers and 4 books on robotics, automation, control, and optimization. His current research emphasizes the development of distributed intelligence technologies and their application to manufacturing automation, robotic systems, digital services, and energy systems management.

Dr. Gruver is a Fellow of the IEEE and the Engineering Institute of Canada. Currently, he is an IEEE Director and Jr. Past President of the IEEE Systems, Man, and Cybernetics Society, for which he previously served as President, Vice President of Long Range Planning and Finance, Vice President Publications, Vice President Conferences, and member of the Board of Governors. He is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics and he co-chairs the SMC Society’s Technical Committee on Distributed Intelligent Systems. He has served as Associate Editor for the IEEE Transactions on Robotics and Automation, Associate Editor of the IEEE Transactions on Control Systems Technology, and he was a founding officer of the IEEE Robotics and Automation Society.