ACM Computing Surveys 28(4es), December 1996, http://www.acm.org/pubs/citations/journals/surveys/1996-28-4es/a4-doyle/. Copyright © 1996 by the Association for Computing Machinery, Inc. See the permissions statement below. This article derives from a position statement prepared for the Workshop on Strategic Directions in Computing Research.


Cleaving (Unto) Artificial Intelligence


Jon Doyle

Massachusetts Institute of Technology, Laboratory for Computer Science
545 Technology Square, Cambridge, MA 02139-3539, USA
doyle@medg.lcs.mit.edu, http://www.medg.lcs.mit.edu/doyle



Abstract: To survive and thrive, whether intellectually or as a force in industry, artificial intelligence (AI) must better identify its unique contributions. Failing this, it faces disrespect, decay, and dissolution. I believe one obtains a better identification both by dividing AI's subjects of study into the categories of rational psychology, psychological engineering, and articulating intelligence than by highlighting AI's characteristic methods of seeking computational-complexity explanations and simulating extremely complex semi-numerical models.

Major fields cannot survive without major problems or methods unique unto themselves. AI's recent and traditional difficulties lie in lacking such unique problems and methods, at least in the view of outsiders. The field has never agreed on its own definition, other than by stipulating that it traditionally encompasses several subjects, notably ``making intelligent machines'' and ``understanding intelligence''. These problems, however, do not possess the uniqueness requisite to a healthy discipline.

Even if we abandon these superficial but traditional ``mission statements'' and look more deeply, we still see that AI shares most of its major intellectual problems with other fields: understanding knowledge, reasoning, and rationality with logic, philosophy, psychology, economics, and sociology; understanding perception, motion, and manipulation with physiology, anatomy, and psychology; understanding language with linguistics and philosophy; understanding planning with economics and operations research. Surely no important aspect of what makes humans special has remained foreign to other fields, for---knowledge aside---people today have the same functions and abilities they have had for thousands of years.

Looking back at the history of the field, the methods of AI appear more distinctive than the problems addressed.

One need not search far for the difficulty posed by relying on methodical distinctions for intellectual survival; most people with problems will adopt any methods they find useful, and some of the hidden success of AI has been evident in the degree to which psychology, philsophy, lingustics, etc., have adopted the modeling and explanatory approaches championed by AI. But once the methods of AI diffuse among the fields, what role remains for AI? Its main reaction to date to this diffusion of method---identifying specific techniques (rules, frames, what have you) as AI and all others as not---has been distinctly damaging to its credibility, especially as the specific mechanisms exploited by AI have precursors and in some cases independent developments in more traditional fields. Thus these useful and important methods do not promise to ensure the existence of AI. Stick with them alone, and the field will continue to fragment, with other fields absorbing (or ignoring) the fragments.

For these reasons, AI must rethink itself and either identify problems unique to the field or give up and go home to the traditional fields. I entertain the possibility that the option of dissolution might prove right, but believe that AI should first try reviewing its purpose or purposes. I propose to abandon the expiring patent on the methods of AI and to find truly unique roles for AI along a cleavage induced by AI's broad conception of intelligence.

I see AI's most dramatic break with the past not in its computational methods but in its conception of intelligence divorced from embodiments in the people and creatures we find around us. Rather than limit attention to what already exists, AI contemplates what might, and views human and other extant intelligences as particular forms of a broader universal. No field before AI has studied the question of understanding intelligence construed this broadly, though philosophers and science-fiction writers speculate on special possibilities. The first step to finding uniqueness in AI lies in distilling out the problems related to intelligence broadly construed. Do this, and two natural fields emerge.

This cleavage of the field requires only two additional elements to capture essentially all of AI. Part of AI studies computational models of human psychologies, but I classify this as a shared subfield of psychology proper, with its traditional presence within AI an artifact mainly of the desirability of sharing computers and code. In the modern computing environment, these tethers loosen daily, and I expect this part of the field quickly to return to psychology itself, if indeed it ever really left it. The remaining element, seen most visibly in the areas of knowledge-based systems and commonsense knowledge, consists of the activity of articulating intelligence, of codifying common and refined knowledge and methods in all topics of human endeavor. Long antedating computers, this work traditionally goes on in every field, and grows more formal and explicit over time. I believe AI has significant leverage to exert on other fields here, by propagating its techniques for formal representations of knowledge. As long as rational psychology and psychological engineering remain unique enterprises and continue to bear fruit, work on articulating intelligence and casting it in new forms will remain a unique activity of the field.

AI might survive without rethinking itself, but only by virtue of increasingly hyperbolic advertising, or by the personal longevity of its adherents. But this sad fate seems unworthy of the gems obscured by the overburden of old tales about the aims and contributions of AI. To survive, AI must offer problems and methods other fields can respect, and it must share knowledge and technique with these fields when it also shares their problems. The latter transformation of AI, from intellectual isolate to conceptual trader, has been accelerating for some time, so its survival reduces to ensuring that it has something left at the end of this exchange. People may disagree about whether rational psychology and psychological engineering provide the right cleavage of AI's gems. But these fields do possess important, clearly understandable tasks all their own. I propose AI reset its work to expose these gems, leave its intellectual parents and cleave to this new joint identity.

Acknowledgments: I thank Joseph Schatz for valuable discussions and MIT for its support over the years. The position argued here restates one presented in papers of 1982--1994 listed below. The image of cleaving the field draws inspiration from George Miller's essay on ``dismembering cognition.''

Bibliography

  1. Doyle, J., 1982. The foundations of psychology: a logico-computational inquiry into the concept of mind, CMU CSD, Report 82-149. Revised version published in Philosophy and AI: Essays at the Interface (R. Cummins and J. Pollock, eds.), Cambridge: MIT Press (1991), 39-77.
  2. Doyle, J., 1983. What is rational psychology? toward a modern mental philosophy, AI Magazine, V. 4, No. 3, 50-53.
  3. Doyle, J., 1988. Big problems for artificial intelligence, AI Magazine, Vol. 9, No. 1, 19-22.
  4. Doyle, J., 1994. Reasoned assumptions and rational psychology, Fundamenta Informaticae, Vol. 20, No. 1 (Spring 1994).
  5. Miller, George A., 1986. Dismembering cognition, in One Hundred Years of Psychological Research in America (Hulse, S. H. and B. F. Green, Jr., eds.). Baltimore: Johns Hopkins University Press, pp. 277-298.


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