Proc. ACL 1-8

In: Brady et al., editor, Robot Motion: Planning and Control, pages 473-498, MIT Press. See also: Hierarchical task and motion planning in the now. IEEE International Conference on Robotics and Automation (ICRA), 2011 (https://ieeexplore.ieee.org/abstract/document/5980391) (https://people.csail.mit.edu/lpk/papers/hpnICRA11Final.pdf). Planning in the Know: Hierarchical belief-space task and motion planning, 2011 (http://mobilemanipulation.info/icra2011/images/7/78/ICRA_2011_Kaelbling_.pdf). Integrated robot task and motion planning in belief space. MIT-CSAIL-TR-2012-019, 2012 (http://dspace.mit.edu/handle/1721.1/71529). Integrated Robot Task and Motion Planning in the Now. MIT-CSAIL-TR-2012-018, 2012 (http://dspace.mit.edu/handle/1721.1/71521). Domain and Plan Representation for Task and Motion Planning in Uncertain Domains, 2011 (http://ias.informatik.tu-muenchen.de/_media/events/knowledge-workshop-iros2011/kaelbling.pdf).

See also: Proceedings of the National Academy of Sciences USA 79, 2554-2558

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.

The methods used for solving equations are described, together with the service facilities. The principal technique, meta-level inference, appears to have applications in the broader field of symbolic and algebraic manipulation. Acknowledgements This work was supported by SERC grants GR/BI29252 and GR/B/73989 and various studentships. Keywords equation solving, rewrite rules, meta-level inference, logic programming I. Introduction The PRESS program was originally developed with two aims in mind. The first aim was to use the program as a vehicle to explore some ideas about controlling search in mathematical reasoning using meta-level descriptions and strategies.

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.

The MIT Press.

"The purpose of this paper is to compare various approaches to generalization in terms of a single framework. Toward this end, generalization is cast as a search problem, and alternative methods for generalization are characterized in terms of the search strategies that they employ. This characterization uncovers similarities among approaches, and leads to a comparison of relative capabilities and computational complexities of alternative approaches. The characterization allows a precise comparison of systems that utilize different representations for learned generalizations."Artificial Intelligence, 18 (2), 203-26.

Artificial Intelligence 19 (1), 17-37

We briefly describe the logic programming language PROLOG concentrating on those aspects of the language that make it suitable for implementing expert systems. We show how features of expert systems such as: (1) inference generated requests for data, (2) probabilistic reasoning, (3) explanation of behaviour can be easily programmed in PROLOG. We illustrate each of these features by showing how a fault finder expert could be programmed in PROLOG.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.