Opening Words of the Chair.

As a chair of the Third Oregon Symposium on Logic, Design and Learning, I welcome everybody very cordially and wish us all true successes in our presentations and constructive progress in discussions.

This symposium is an outgrowth of work of Prof. Perkowski's group at PSU, as well as their collaborators outside PSU, on applying logic synthesis methods to the areas outside traditional circuit design.

We believe that the methods developed in design automation are powerful and can be successfully used in a number of areas outside logic synthesis. EDA researchers developed efficient data structures such as various decision diagrams, general synthesis paradigms such as functional decomposition, useful concepts such as relational descriptions of systems; and fundamental algorithms such as satisfiability.

Although there is a one-to-one mapping between Logic Synthesis and Constructive Induction methods of Machine Learning; although there are strong links between Logic Synthesis and Rough Set Theory, the researchers in these areas are rarely aware that they talk about the same things using different technical terminology. We believe that strong synergy between these fields is possible. Logic Synthesis can also learn from other areas. For example, information theory methods used for years in Decision Tree Analysis and Machine Learning can be used in Functional Decomposition.

When we take a basic textbook in pattern recognition, robotics, or neural networks, we are surprised how much of the material is based on the same problems as logic synthesis: graph coloring, set covering, cliques, search, satisfiability. On the other hand, it seems that the algorithms developed by researchers in logic are more mature and data structures they use is more sophisticated than in the neighboring areas. For instance, BDDs are now being used in a number of areas such as theorem proving, while model checking methods developed in formal verification of hardware can be applied to robotics. As another example, covering problems that are solved in robotics have a limited size due to the use of obsolete algorithms. Now, with the introduction of implicit algorithms to solve unate and binate covering, we can handle much larger task encountered in coordinating robot movements.

The goal of the symposium is to investigate these and other mutual links between logic, design, and learning. Logic is a fundament of design and learning and its role in many practical design areas is crucial, as exemplified by the proliferation of logic programming and constraint programming languages. Recent research shows that logical methods have their place in learning, and the importance of learning methods in design also becomes visible, as evidenced by the emergence of the recursive learning paradigm in testing.

We believe that this synergy of logic, design, and learning is more general than only logic synthesis for electric circuits, and that it is applicable to multi-valued and fuzzy networks, arithmetic logic and other systems used in control, robotics, and image processing. For example, using Walsh transform in logic design, image processing and genetic algorithms, uniform decomposition methods for binary, multi-valued, and fuzzy functions and relations, or arithmetic decision diagrams in Artificial Intelligence, can be only a one out of many possible applications.

The other issue, which we want to discuss in this symposium, is the relation between university and industrial research. Oregon quickly becomes the Mecca of design automation industry with companies such as Mentor, Synopsys, Cadence, and Intel but there has been no local forum to discuss theoretical problems of applied research. On the other hand, academic researchers, permanently in need of problems to solve, may turn to problems that are not practically useful.

For instance, does anybody in industry use Spectral Methods for synthesis and analysis of switching functions? In the past, the question was often asked, "Does anybody in industry use EXOR logic"? Now we know that the answer is Yes, and many would like to know, which university research areas are of primary interest to industry and why.

Many difficult problems in industry can be solved by academicians with good mathematics and algorithm background. At this Symposium, we try to have an honest and tough discussion - how can these two groups, industry and academia, help one another? What are the most difficult issues in industry, the toughest nuts to crack? To this purpose, we invited several researchers from leading companies to help us answer these and other questions.

Next, in the recent years, we observe the growing influence of biologically inspired ideas in the areas of logic, design, and learning: set logic, fuzzy logic, evolutionary computing, artificial neural nets, to name only a few. Because logic methods influence Data Mining, Knowledge Discovery in Data Bases, Pattern Recognition and Machine Learning, they will undoubtedly influence the area of robotics. And here they will intersect with biologically-motivated design and learning. The area of Intelligent Robotics, that will hopefully grow in Portland, will have to incorporate these biological and logical sources; the Electric Horse project to build a robotic horse that will participate in parade at Rose Festival is the first example of these efforts.

Finally, we believe in a new kind of conference. All papers have been carefully reviewed and put on the webpage of the Symposium. The remarks and opinions have been added to them, so that everybody could be familiar with the material before the conference. The printed Proceedings are published in the traditional manner. Standard sessions, panel sessions, tutorials, and exhibitions are organized with the goals of achieving open tasks, such as building a "robotic dog" for high-schools, or a robotic image processing software, rather than only discussing incremental research problems.

We would like to encourage the participants to share their software and robotic kits, so that every second year that this symposium is organized, we could learn more about Logic, Design, and Learning, and grow towards the same goals as a community. We hope that the result will be common research projects and publications, joint efforts to write software and build robots.

Alan Mishchenko

Chair, LDL 2000