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.

J. International Computer Chess Association, May, 131–139.

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.

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.

We outline an approach for reasoning about events and time within a logic programming framework. The notion of event is taken to be more primitive than that of time and both are represented explicitly by means of Horn clauses augmented with negation by failure. The main intended applications are the updating of databases and narrative understanding. In contrast with conventional databases which assume that updates are made in the same order as the corresponding events occur in the real world, the explicit treatment of events allows us to deal with updates which provide new information about the past. Default reasoning on the basis of incomplete information is obtained as a consequence of using negation by failure.

In Kanal, L. N. and Lemmer, J. F. (Eds.), UAI 2, pp. 137–151. Elsevier/North-Holland.

In AAAI-86, pp. 1041–1045.

Review of Readings in Medical Artificial Intelligence: The First Decade

With the great amount of attention now being paid by the media to AI, it would be naive, shortsighted, and even self-deceptive to think that there will not be public interest in scrutinizing, monitoring, regulating, and even constraining our efforts. What we do can affect people's lives as they understand them. People are going to ask not only what we are doing but also whether it should be done. We should be prepared to participated in open discussion and debate on such ethical issues.

Many of the ideas behind object-oriented programming have roots going back to SIMULA. The first substantial interactive, display-based implementation was the SMALLTALK language. The object-oriented style has often been advocated for simulation programs, systems programming, graphics, and AI programming. It is also related to a line of work in AI on the theory of frames and their implementation in knowledge representation languages such as KRL, KEE, FRL, and UNITS.