This chapter from the Mycin book is a brief overview of van Melle's Ph.D. dissertation (Stanford, Computer Science), and is a shortened and edited version of a paper appearing in Pergamon-lnfotech state of the art report on machine intelligence, pp. 249-263. Maidenhead, Berkshire, U.K.: Infotech Ltd., 1981.
Ever since the early days of modern computing in the 1940s, the biological metaphor has been irresistible. The first computers — room-size behemoths — were referred to as “giant brains” or “electronic brains,” in headlines and everyday speech. As computers improved and became capable of some tasks familiar to humans, like playing chess, the term used was “artificial intelligence.” DNA, it is said, is the original software.
A brain does its computing with a design drastically different from today’s computers. Its processors — neurons — are, in computing terms, massively distributed; there are billions in a human brain. These neuron processors are wrapped in its data memory devices — synapses — so that the brain’s paths of communication are extremely efficient and diverse, through the neuron’s axons, which conduct electrical impulses.A machine that adopts that approach, Dr. Modha said, would represent “a crucial shift away from von Neumann computing.”
A brain does its computing with a design drastically different from today’s computers. Its processors — neurons — are, in computing terms, massively distributed; there are billions in a human brain. These neuron processors are wrapped in its data memory devices — synapses — so that the brain’s paths of communication are extremely efficient and diverse, through the neuron’s axons, which conduct electrical impulses.
A machine that adopts that approach, Dr. Modha said, would represent “a crucial shift away from von Neumann computing.”
We argue that the time between mission conception and implementation can be radically reduced, that launch mass can be slashed, that totally autonomous robots can be more reliable than ground controlled robots, and that large numbers of robots can change the tradeoff between reliability of individual components and overall mission success. Lastly, we suggest that within a few years it will be possible at modest cost to invade a planet with millions of tiny robots
In this paper we have shown how it is possible to use certain combinators onrelations to produce an interpretation of a class of clauses (Horn Clauses) inpredicate logic. The work was inspired by a particular view of the task of writingcertain kinds of program, but has not yet given rise to a system implementedon a digital computer, although some initial studies have been made.
Includes the Java Agent Framework which allows you to build agents using component-based programming. It is intended primarily as a development kit, not as an out-of-the box agent utility. From the U. Mass. The Multi-Agent Systems Lab. Director: Professor Victor Lesser, Associate Director: Dr. Dan Corkill.
Proceedings of the First Annual Conference on Advances in Cognitive Systems, a meeting held in Palo Alto, California on December 6, 7, and 8, 2012.
From the Essay "Intelligent Behavior in Humans and Machines" by Pat Langley:
In this paper, I review the role of cognitive psychology in the origins of artificial intelligence and in the continuing pursuit of its initial objectives. I consider some key ideas about representation, performance, and learning that had their inception in computational models of human behavior, and I argue that this approach to developing intelligent artifacts, although no longer common, has an important place in cognitive systems. Not only will research in this paradigm help us understand the nature of human cognition, but findings from psychology can serve as useful heuristics to guide our search for accounts of intelligence. I present some constraints of this sort that future research should incorporate, and I claim that another psychological notion - cognitive architecture - is especially relevant to developing unified theories of the mind. Finally, I suggest ways to encourage renewed interaction between AI and cognitive psychology to the advantage of both disciplines.
Alan Turing, who probably got there first no matter how exotic your approach to artificial intelligence, once had the idea of "unorganized machines". He was thinking of possible ways that the initial neural networks might form a newborn baby's brain.
This paper presents a machine designed for compact representation and rapid execution of LISP programs. The machine language is a factor of 2 to 5 more compact than S-expressions or conventional compiled code, and the.compiler is extremely simple. The encoding scheme is potentiall y applicable to data as wel l as program. The machine also provides for user-defined data structures.
This paper considers various factors affecting system organization for speech understanding research. The structure of the Hearsay system based on a set of cooperating, independent processes using the hypothesize-and-test paradigm is presented. Design considerations for the effective use of multiprocessor and network architectures in speech understanding systems are presented: control of processes, interprocess communication and data sharing, resource allocation, and debugging are discussed.
See also: IEEE Xplore.