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A first order formalization of knowledge and action for a multi-agent planning system
We are interested in constructing a computer agent whose behaviour will be intelligent enough to perform cooperative tasks involving other agents like itself. The construction of such agents has been a major goal of artificial intelligence research. One of the key tasks such an agent must perform is to form plans to carry out its intentions in a complex world in which other planning agents also exist. To construct such agents, it will be necessary to address a number of issues that concern the interaction of knowledge, actions, and planning. Briefly stated, an agent at planning time must take into account what his future states of knowledge will be if he is to form plans that he can execute; and if he must incorporate the plans of other agents into his own, then he must also be able to reason about the knowledge and plans of other agents in an appropriate way. In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.
Higher-order extensions to PROLOG: are they needed?
PROLOG is a simple and powerful progamming language based on first-order logic. This paper examines two possible extensions to the language which would generally be considered "higher-order".t The first extension introduces lambda expressions and predicate variables so that functions and relations can be treated as 'first class' data objects. We argue that this extension does not add anything to the real power of the language. The other extension concerns the introduction of set expressions to denote the set of all (provable) solutions to some goal. We argue that this extension does indeed fill a real gap in the language, but must be defined with care.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.
The computational problem of motor control
Motor control systems are complex systems that process information. Orientation behaviour, posture control, and the manipulation of objects are examples of motor control systems which involve one or more sensory modality and various central neural processes, as well as effector systems and their immediate neuronal control mechanisms. Like all complex information processing systems, they must be analysed and understood at several different levels (see, e.g., Marr & Poggio 1977).At the lowest level there is the analysis of basic components and circuits, the neurons, their synapses, etc. At the other extreme, there is the study of the computations performed by the system โ the problems it solves and the ways that it solves them โ and the analysis of its logical organization in terms of its primary modules. In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.
Practical machine intelligence
It appears, however, that we [in AI] are now (finally!) on the verge of practicality in a number of specialities within machine intelligence more or less simultaneously. This can be expected to result in the short term in a qualitative shift in the nature of the field itself, and to result in the longer term in a shift in the way certain industries go about their businessThis paper will discuss three specific areas of work in machine intelligence that MIC [Machine Intelligence Corporation] feels are ripe for commercial application: machine vision, naturallanguage access to computers, and expert systems. It will close with some observations on what makes these areas appropriate for application at this time, and on the difference between a technical solution to a problem and a product.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.
Application of the PROSPECTOR system to geological exploration problems
A practical criterion for the success of a knowledge-based problem-solving system is its usefulness as a tool to those working in its specialized domain of expertise. This paper describes an evaluation and several applications of a knowledge-based system, the PROSPECTOR consultant for mineral exploration. PROSPECTOR is a rule-based judgmental reasoning system that evaluates the mineral potential of a site or region with respect to inference network models of specific classes of ore deposits. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.
Artificial Intelligence at Advanced Information and Decision Systems
Advanced Information and Decision Systems (AI-DS) is a relatively new, employee-owned company that does basic and applied research, product development, and consulting in the fields of artificial intelligence, computer science, decision analysis, operations research, control theory, estimation theory, and signal processing. AI&DS performs studies, analyses, systems design and evaluation, and software development for a variety of industrial clients and government agencies, including the Department of Defense and Energy.
Yale Artificial Intelligence Project (Research in Progress)
The Yale Artificial Intelligence Project, under the direction of Professor Roger C. Schank, supports a number of research projects. Most of this research is in the02-02 area of attempting to model the processes involved in human understanding, with a current emphasis on memory models and the processes involved in learning.
Introducing Carnegie-Mellon University's Robotics Institute (Research in Progress)
Fox, Mark S., Bartel, Gene, Moravec, Hans
Carnegie-Mellon University has established a Robotics Institute to bring its expertise in engineering, science, and industrial administration to bear upon the problem of national industrial productivity. The institute has been established to undertake advanced research and development in seeing, thinking robots and intelligent systems, and to facilitate transfer of this technology to industry. The Institute is engaged in broad programs of research in robotics, artificial intelligence, manufacturing technology, micro-electronics technology, and computer science. The Institute offers the promise of dramatic advances that will not only improve the productivity of all types of employees but also lead to improvements in the "quality of life" for all.
The Fredkin Challenge Match
On August 18 and 19, 1980, at Stanford U n i v e r s i t y during at Stanford U n i v e r s i t y, with the actual game on i n a c l o s e d the A A A I conference, the f i r s t of a projected p a i r of annual room containing o n l y the player, computer terminal operators c h e s s competitions pitting the w o r l d ' s best computer programs (L a r r y A t k i n and David Cahlander of Control Data Corporation) against rated human p l a y e r s of approximately the same and the referee. U p s t a i r s was a large demonstration strength was held. These matches are part of the F r e d k i n p r i z e room where two boards, one for the actual position and one competition, wherein a sum of $100,000, established by the for a n a l y s i s, were used to keep the audience abreast of what F r e d k i n Foundation of Cambridge, Mass, i s to be awarded to was happening and could be expected to happen. The moves the creators of a program that can defeat the W o r l d Chess were communicated through a telecommunications setup Champion i n o f f i c i a l competition. The program i n t h i s match was C H E S S 4.9 of Northwestern In the f i r s t game, C H E S S 4.9 played the White s i d e of a U n i v e r s i t y, authored by David S l a t e and L a r r y A t k i n.