The Center for Automation and Intelligent Systems Research, Case Western Reserve University

AI Magazine

The Center for Automation and Intelligent Systems Research at Case Western Reserve University, founded in 1984, provides the setting and the administrative and funding mechanisms for coordinating and focusing the capabilities of faculty members and students from many disciplines and departments to deal with significant realworld problems encountered in the automation of production. The center serves as an interface between separate basic research efforts in the various disciplines and academic departments and the multidisciplinary group efforts needed to deal effectively with nontrivial real problems.


The Next Knowledge Medium

AI Magazine

The most widely understood goal of artificial intelligence is to understand and build autonomous, intelligent, thinking machines. A perhaps larger opportunity and complementary goal is to understand and build an interactive knowledge medium.


Legged Robots That Balance

Classics

This book, by a leading authority on legged locomotion, presents exciting engineering and science, along with fascinating implications for theories of human motor control. It lays fundamental groundwork in legged locomotion, one of the least developed areas of robotics, addressing the possibility of building useful legged robots that run and balance. The book describes the study of physical machines that run and balance on just one leg, including analysis, computer simulation, and laboratory experiments. Contrary to expectations, it reveals that control of such machines is not particularly difficult. It describes how the principles of locomotion discovered with one leg can be extended to systems with several legs and reports preliminary experiments with a quadruped machine that runs using these principles.


Derivational analogy: A theory of reconstructive problem solving and expertise acquisition

Classics

CMU-CS-85-115, Carnegie Mellon University. Reprinted in Michalski, R. S., Carbonell, J. G., and Mitchell, T. M., (Eds.), Machine Learning: An Artificial Intelligence Approach, volume 2, chapter 14, pages 371-392. Morgan Kaufmann Publishers. Derivational analogy, a method of solving problems based on the transfer of past experience to new probiem situations, is discussed in the context of other general approaches to problem solving. The experience transfer process consists of recreating lines of reasoning, including decision sequences and accompanying justifications, that proved effective in solving particular problems requiring similar initial analysis. The role of derivational analogy in case-based reasoning and in automated expertise acquisition is discussed.


Parallel Distributed Processing

Classics

What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind. The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations.