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Mind over machine: The power of human intuition and expertise in the era of the computer
135
ARTIFICIAL INTELLIGENCE
Book Review Hubert L. Dreyfus and Stuart E. Dreyfus, Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer (Basil Blackweli, Oxford, 1986); 223 pages, £15.00. Reviewed by: Timothy D. Koschmann Xerox Corporatton, XAIS, Des Plaines, IL 60018, U.S.A.
In Critique of Machine Reason Overview Hubert Dreyfus has acquired a reputation as being one of the most vehement and outspoken critics of the goals and accomplishments of research in artificial intelligence. The recently published book by Hubert and Stuart Dreyfus, Mmd over Machine, expands upon the theme expressed in earlier works [2] that AI systems are based upon an overly simplistic model of human problem solving and as a result will never achieve levels of performance approaching those of human beings. Mmd over Machine can be viewed as having three major sections. A five-stage model for the acquisition of human problem solving skills is presented in Chapter 1. Chapters 2, 3, and 4 catalog the failings of artificial intelligence research to produce a true, reasonmg program. The authors contrast systems based on "holistic" principles with those utihzing "mechanistic" principles. Holograms are presented as a type of metaphor for a system based on hohstic principles. Chapters 5 and 6 contain critical evaluations of applications of computers in education and decision management. The current review will concentrate on the first two sections which are most directly relevant to expert system research. The book begins with an exploration of what are seen to be the philosophical foundations of the AI research movement. Artificial intelhgence, according to the authors, has its roots in the "rationalist" tradition in western philosophy characterized by thinkers such as Plato, Descartes, Leibnlz, Kant and Husserl. Attempts to emulate the behavior of a human expert by abstracting a collection of rules is seen to be closely related to the goals of earlier philosophers trymg to identify the underlying principles of all knowledge. The authors state that Arttfictal Intelhgence 33 (1987) 135-140 0004-3702/87/$3 50 © 1987, Elsevier Science Pubhshers B V (North-Holland)
136
BOOK REVIEW
the problem with rahonalistic philosophies and, by extension, current research in artificial intelligence is that basic knowledge cannot be reduced to any simple set of principles. The argument presented is that if there is m o r e to expertise than can be captured in a set of rules, then rule-based systems will never be able to rival the performance of true experts, no matter how many rules are added to their knowledge bases.
Five stages in the acquisition of expertise The book asserts that advanced problem solving skdl is acquired in five incremental states entitled Novice, Advanced Beginner, Competency, Proficiency and Expertise. A student learning a new high-level skill, be it chess playing or medical diagnosis, must progress through these five states. In the Novice stage, skill is obtained by following a set of learned rules. The rules used are termed "context-free" rules because they can be applied without reference to the surrounding context. For instance, a beginning chess player might be taught that a rook is worth half a queen m a trade. For a beginner this simple rule would suffice without qualifications concerning other pieces on the board, whether you were in the middle game or end game, etc. As beginners acquire experience, they learn more sophisticated rules. These rules require that the student recognize salient features of the context in which the rules are applied. The authors refer to these as "situational" cues to differentiate them from "context-free" cues used in the Novice stage. An example of a situational cue provided in the text would be when a chess player recognizes an " o v e r e x t e n d e d " posit~on. The player may not be able to verbalize what constitutes an overextended position, but experience has taught him how to recognize one and what to do about it. Competency is attained in the five-stage model when the student can organize his/her rules into a hierarchical structure, such that different rule sets are applied depending on the situation introducing the notion of "perspective " By virtue of acquired experience the student can now view a problem from a variety of perspectives. When the student selects a particular perspective, a specific set of rules are activated. The authors cite the example of a chess player deciding to do a king-side attack Rules concerning equality of trades and maintenance of position are overridden by the requirements of the chosen strategy. The stage entitled Proficiency is just a transition level between Competency and Expertise. What separates the truly expert p e r f o r m e r from the merely competent in this scheme is the notion of intuition. The authors summarize their theory of the acquisition of expertise by saying. " C o m p e t e n t performance is rational; proficiency is transitional; experts act arationally." The expert does not analytically process a set of rules to solve a problem but instead leaps directly to the solution. Task performance is swift and fluid Blitz chess players
BOOK REVIEW
137
do not have time to analytically evaluate a position. Players who excell at five-minute games must resort to some other means of selecting moves. How does one develop a computer program that reasons "arationally"? Here M i n d over M a c h i n e is a bit sketchy. Holograms are presented as a type of metaphor for how information could be represented in a "holistic" way. One of the properties of a hologram is that each piece contains a full picture of the whole. If you shatter a hologram picture and shine a laser through one of the pieces you will still be able to discern the whole picture. Another interesting characteristic of holograms is the way that interference patterns develop when two holographic images are superimposed. The authors view this latter novelty of holographic images as an example of a type of feature-free pattern recognition. But how does one make the transition from holograms to expert systems? No explicit prescriptions are made here but there are several clues provided. Decentrahzed systems with a high degree of parallehsm are favored. Systems that use rules to manipulate symbols are considered obsolete. Symbol manipulating systems depend on "context-free" associations and as such can never get us where we want to go. W h y we h a v e n ' t reached the m o o n
In one of his earlier books Dreyfus [2] likened research efforts in AI to climbing a tree in hopes of reaching the moon. Because the models upon which AI researchers were designing their systems were considered inadequate, presumably no amount of effort could get these systems to emulate truly intelhgent behavior. Now in M i n d over M a c h i n e he is arguing that the field of artificial intelligence is played out, that all of the easy battles have been won and the war is destined to end in defeat. Why the pessimism? If the five-stage model for the acquisition of expertise is correct, then systems that reason from rules will never be able to advance to the level of Expert. But in M i n d over M a c h i n e the argument is proved in reverse. The authors wish to assert the antecedent by demonstrating that expert systems have not satisfactorily met expectations. In the absence of direct empirical support for their theory the authors point to the existence of unsolved problems in artificial intelhgence research as evidence that current models of expertise are faulty. According to the authors, the failure of researchers in AI to find solutions for these problems suggests that the problems are unsolvable given current methodology. Let us look at these problems in turn. The first problem is that it has proved to be very difficult to instill common sense into AI systems. The " c o m m o n sense problem" has many variations. Semantic disambiguation in computational linguistics calls for some form of common sense, as does continuous speech understanding and expert systems that can recognize the extent of their expertise. The abdity to represent and
138
BOOK REVIEW
reason from common sense is central to much of what is being done m AI today. Although the mability of AI systems to represent common sense ~s clearly a real problem, it is not a problem that has gone unnoticed by researchers in the field. There is and has been intense interest in developing techniques for representing common sense knowledge. A second problem in AI research discussed in M m d over M a c h i n e is referred to as the problem of "changing relevance." Like the common sense problem, this problem has several forms In the AI literature it is usually discussed as the "frame problem." In simple terms it is the problem of maintaining the validity of a database over time while some of its underlying assumptions may be changed. Several solutions have been proposed for this problem including nonmonotonic logics and truth maintenance systems. While it is true that both of these problems fall into the category of " o p e n " research issues, the question is what significance do they hold for the future vlabihty of AI research 9 The authors of M m d over Ma ch i n e would assert that because they haven't been solved yet they are unhkely to be solved at all. However, given the number of researchers actively working on various aspects of both problems, it would appear somewhat premature to declare them unsolvable just yet. M i n d over M a c h m e specifically cites several well-known systems that "prove the case" that expert systems have failed to meet expectlons. Although it ~s allowed that MACSYMA has proved to be a useful system, it is d~sm~ssed as an AI application because it resorts to using algorithmic solutions. DENDRAL is criticized because it has never been used commercially on a w~de scale. The success of R1 (XCON) is attributed to the fact that the problem of configuring VAX systems is combinatorial in nature and lends itself to solution by an expert system. Modern chess playing programs such as Cray-Blitz are crmc~zed for depending on brute-force search methods and for lacking long-term strategies. The performance of medical dmgnostic consultants like MYCIN and INTERNIST ~s said to lag behind that of human experts. Although all of these systems are well known, none of them could be considered to be examples of the state of the art. MACSYMA, DENDRAL, and MYCIN are usually discussed now as examples of "classic" systems befitting the seminal role that they played in the development of the technology. The authors might have done better had they evaluated systems that have been developed in the last five years. Studies that compare the performance of physicians and medical expert systems are often difficult to interpret. When human experts cannot always come to agreement on a given diagnosis, what represents the "right" answer? In a carefully controlled study where every diagnosis was confirmed surgacally, a computer-based system was found to perform better than the human experts [1]. The relative performance of a computer-based system will be largely determined by the diagnostic model by which the system was constructed.
BOOK REVIEW
139
Systems based on a single physician's heuristic rules could not be expected to surpass the physician in performance. Under such circumstances, the level of performance of the domain expert might be considered an asymptotic limit for the performance of the resulting expert system. It is only when the expert system has access to more information than the domain expert that a computer-based system can exceed the performance of a human expert. Such was the case in the deDombal [1] study, that used a clinical database to compute statistics that were used to perform the diagnostic classifications. There are many reasons why the authors might have had a difficult time finding examples of expert systems that are being actively used in industry. Most of these reasons have nothing to do with the competence of the systems or their underlying models. Early expert systems were research prototypes and were not ready for distribution for use at other sites. Also, development and maintenance of large expert systems has traditionally been an expensive proposition. Finally, companies that have developed proprietary knowledgeengineering projects are sometimes reluctant to discuss them. However, instances of expert systems being productively used in industry do exist. For example, synthetic chemists working for a consortium of European pharmaceutical companies have been using the SECS system [6] for a number of years.
Discussion
What then can we conclude about all of this? Many leading researchers in AI think of Dreyfus as something of a gadfly [4]. They consider Dreyfus' polemic against AI research to be at least naive, if not irresponsible, and any attempt to respond to it would only lend credence to his unfounded arguments. On the other side there are those who view Dreyfus as the leading proponent of a new philosophy of cognitive science. Excerpts from M i n d over M a c h i n e have begun to appear in trade journals and the popular press [3]. M i n d over M a c h i n e has become required reading for some graduate courses in expert systems. In the end, either course is probably unwise. To ignore this book and Dreyfus' earlier writings on AI would be to sacrifice the unique and important perspective of an informed outside observer. However, to accept uncritically Dreyfus' jaundiced view of the field would be equally costly Dreyfus has set out to make an argument that AI doesn't work. His biased assessment of the accomphshments of workers in the field is excessively critical. Expert system research has already produced practical results. The list of existing expert systems is large and growing rapidly [5]. However, it is true that research in expert systems and in AI in general could use some direction. Dreyfus' criticisms can help us find that direction by helping us to focus on some of the critical issues for future research.
140
BOOK REVIEW REFERENCES
1 deDombal, F T , Leaper, D J , Horrocks, J C , Stanlland, J R and McCann, A P , Human and computer-aided diagnosis of abdominal pare Further report with emphasis on performance of chmclans, Brtttsh Medical J (1974) 376-380 2 Dreyfus, H L , What Computers Can't Do A Crmque of Artlficlal Reason (Harper & Row, New York, 1972) 3 Dreyfus, H and Dreyfus, S , Why expert systems do not exhibit expertise, IEEE Expert 1 (2) (1986) 86-90 4 McCorduck, P , Machmes Who Thmk A Personal Inquiry mto the History and Prospects o I Artlfictal lntelhgence (Freeman, New York, 1979) 5 Waterman, D , Understanding Expert Systems (Addxson-Wesley, Reading, MA, 1986) 6 Wlpke, W T , Ouch1, G I and Knshnan, S , SECS An apphcatlon of artificial mtelhgence techmques, Artificial lntelhgence 11 (1978) 173-193
ARTIFICIAL INTELLIGENCE
Book Review Hubert L. Dreyfus and Stuart E. Dreyfus, Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer (Basil Blackweli, Oxford, 1986); 223 pages, £15.00. Reviewed by: Timothy D. Koschmann Xerox Corporatton, XAIS, Des Plaines, IL 60018, U.S.A.
In Critique of Machine Reason Overview Hubert Dreyfus has acquired a reputation as being one of the most vehement and outspoken critics of the goals and accomplishments of research in artificial intelligence. The recently published book by Hubert and Stuart Dreyfus, Mmd over Machine, expands upon the theme expressed in earlier works [2] that AI systems are based upon an overly simplistic model of human problem solving and as a result will never achieve levels of performance approaching those of human beings. Mmd over Machine can be viewed as having three major sections. A five-stage model for the acquisition of human problem solving skills is presented in Chapter 1. Chapters 2, 3, and 4 catalog the failings of artificial intelligence research to produce a true, reasonmg program. The authors contrast systems based on "holistic" principles with those utihzing "mechanistic" principles. Holograms are presented as a type of metaphor for a system based on hohstic principles. Chapters 5 and 6 contain critical evaluations of applications of computers in education and decision management. The current review will concentrate on the first two sections which are most directly relevant to expert system research. The book begins with an exploration of what are seen to be the philosophical foundations of the AI research movement. Artificial intelhgence, according to the authors, has its roots in the "rationalist" tradition in western philosophy characterized by thinkers such as Plato, Descartes, Leibnlz, Kant and Husserl. Attempts to emulate the behavior of a human expert by abstracting a collection of rules is seen to be closely related to the goals of earlier philosophers trymg to identify the underlying principles of all knowledge. The authors state that Arttfictal Intelhgence 33 (1987) 135-140 0004-3702/87/$3 50 © 1987, Elsevier Science Pubhshers B V (North-Holland)
136
BOOK REVIEW
the problem with rahonalistic philosophies and, by extension, current research in artificial intelligence is that basic knowledge cannot be reduced to any simple set of principles. The argument presented is that if there is m o r e to expertise than can be captured in a set of rules, then rule-based systems will never be able to rival the performance of true experts, no matter how many rules are added to their knowledge bases.
Five stages in the acquisition of expertise The book asserts that advanced problem solving skdl is acquired in five incremental states entitled Novice, Advanced Beginner, Competency, Proficiency and Expertise. A student learning a new high-level skill, be it chess playing or medical diagnosis, must progress through these five states. In the Novice stage, skill is obtained by following a set of learned rules. The rules used are termed "context-free" rules because they can be applied without reference to the surrounding context. For instance, a beginning chess player might be taught that a rook is worth half a queen m a trade. For a beginner this simple rule would suffice without qualifications concerning other pieces on the board, whether you were in the middle game or end game, etc. As beginners acquire experience, they learn more sophisticated rules. These rules require that the student recognize salient features of the context in which the rules are applied. The authors refer to these as "situational" cues to differentiate them from "context-free" cues used in the Novice stage. An example of a situational cue provided in the text would be when a chess player recognizes an " o v e r e x t e n d e d " posit~on. The player may not be able to verbalize what constitutes an overextended position, but experience has taught him how to recognize one and what to do about it. Competency is attained in the five-stage model when the student can organize his/her rules into a hierarchical structure, such that different rule sets are applied depending on the situation introducing the notion of "perspective " By virtue of acquired experience the student can now view a problem from a variety of perspectives. When the student selects a particular perspective, a specific set of rules are activated. The authors cite the example of a chess player deciding to do a king-side attack Rules concerning equality of trades and maintenance of position are overridden by the requirements of the chosen strategy. The stage entitled Proficiency is just a transition level between Competency and Expertise. What separates the truly expert p e r f o r m e r from the merely competent in this scheme is the notion of intuition. The authors summarize their theory of the acquisition of expertise by saying. " C o m p e t e n t performance is rational; proficiency is transitional; experts act arationally." The expert does not analytically process a set of rules to solve a problem but instead leaps directly to the solution. Task performance is swift and fluid Blitz chess players
BOOK REVIEW
137
do not have time to analytically evaluate a position. Players who excell at five-minute games must resort to some other means of selecting moves. How does one develop a computer program that reasons "arationally"? Here M i n d over M a c h i n e is a bit sketchy. Holograms are presented as a type of metaphor for how information could be represented in a "holistic" way. One of the properties of a hologram is that each piece contains a full picture of the whole. If you shatter a hologram picture and shine a laser through one of the pieces you will still be able to discern the whole picture. Another interesting characteristic of holograms is the way that interference patterns develop when two holographic images are superimposed. The authors view this latter novelty of holographic images as an example of a type of feature-free pattern recognition. But how does one make the transition from holograms to expert systems? No explicit prescriptions are made here but there are several clues provided. Decentrahzed systems with a high degree of parallehsm are favored. Systems that use rules to manipulate symbols are considered obsolete. Symbol manipulating systems depend on "context-free" associations and as such can never get us where we want to go. W h y we h a v e n ' t reached the m o o n
In one of his earlier books Dreyfus [2] likened research efforts in AI to climbing a tree in hopes of reaching the moon. Because the models upon which AI researchers were designing their systems were considered inadequate, presumably no amount of effort could get these systems to emulate truly intelhgent behavior. Now in M i n d over M a c h i n e he is arguing that the field of artificial intelligence is played out, that all of the easy battles have been won and the war is destined to end in defeat. Why the pessimism? If the five-stage model for the acquisition of expertise is correct, then systems that reason from rules will never be able to advance to the level of Expert. But in M i n d over M a c h i n e the argument is proved in reverse. The authors wish to assert the antecedent by demonstrating that expert systems have not satisfactorily met expectations. In the absence of direct empirical support for their theory the authors point to the existence of unsolved problems in artificial intelhgence research as evidence that current models of expertise are faulty. According to the authors, the failure of researchers in AI to find solutions for these problems suggests that the problems are unsolvable given current methodology. Let us look at these problems in turn. The first problem is that it has proved to be very difficult to instill common sense into AI systems. The " c o m m o n sense problem" has many variations. Semantic disambiguation in computational linguistics calls for some form of common sense, as does continuous speech understanding and expert systems that can recognize the extent of their expertise. The abdity to represent and
138
BOOK REVIEW
reason from common sense is central to much of what is being done m AI today. Although the mability of AI systems to represent common sense ~s clearly a real problem, it is not a problem that has gone unnoticed by researchers in the field. There is and has been intense interest in developing techniques for representing common sense knowledge. A second problem in AI research discussed in M m d over M a c h i n e is referred to as the problem of "changing relevance." Like the common sense problem, this problem has several forms In the AI literature it is usually discussed as the "frame problem." In simple terms it is the problem of maintaining the validity of a database over time while some of its underlying assumptions may be changed. Several solutions have been proposed for this problem including nonmonotonic logics and truth maintenance systems. While it is true that both of these problems fall into the category of " o p e n " research issues, the question is what significance do they hold for the future vlabihty of AI research 9 The authors of M m d over Ma ch i n e would assert that because they haven't been solved yet they are unhkely to be solved at all. However, given the number of researchers actively working on various aspects of both problems, it would appear somewhat premature to declare them unsolvable just yet. M i n d over M a c h m e specifically cites several well-known systems that "prove the case" that expert systems have failed to meet expectlons. Although it ~s allowed that MACSYMA has proved to be a useful system, it is d~sm~ssed as an AI application because it resorts to using algorithmic solutions. DENDRAL is criticized because it has never been used commercially on a w~de scale. The success of R1 (XCON) is attributed to the fact that the problem of configuring VAX systems is combinatorial in nature and lends itself to solution by an expert system. Modern chess playing programs such as Cray-Blitz are crmc~zed for depending on brute-force search methods and for lacking long-term strategies. The performance of medical dmgnostic consultants like MYCIN and INTERNIST ~s said to lag behind that of human experts. Although all of these systems are well known, none of them could be considered to be examples of the state of the art. MACSYMA, DENDRAL, and MYCIN are usually discussed now as examples of "classic" systems befitting the seminal role that they played in the development of the technology. The authors might have done better had they evaluated systems that have been developed in the last five years. Studies that compare the performance of physicians and medical expert systems are often difficult to interpret. When human experts cannot always come to agreement on a given diagnosis, what represents the "right" answer? In a carefully controlled study where every diagnosis was confirmed surgacally, a computer-based system was found to perform better than the human experts [1]. The relative performance of a computer-based system will be largely determined by the diagnostic model by which the system was constructed.
BOOK REVIEW
139
Systems based on a single physician's heuristic rules could not be expected to surpass the physician in performance. Under such circumstances, the level of performance of the domain expert might be considered an asymptotic limit for the performance of the resulting expert system. It is only when the expert system has access to more information than the domain expert that a computer-based system can exceed the performance of a human expert. Such was the case in the deDombal [1] study, that used a clinical database to compute statistics that were used to perform the diagnostic classifications. There are many reasons why the authors might have had a difficult time finding examples of expert systems that are being actively used in industry. Most of these reasons have nothing to do with the competence of the systems or their underlying models. Early expert systems were research prototypes and were not ready for distribution for use at other sites. Also, development and maintenance of large expert systems has traditionally been an expensive proposition. Finally, companies that have developed proprietary knowledgeengineering projects are sometimes reluctant to discuss them. However, instances of expert systems being productively used in industry do exist. For example, synthetic chemists working for a consortium of European pharmaceutical companies have been using the SECS system [6] for a number of years.
Discussion
What then can we conclude about all of this? Many leading researchers in AI think of Dreyfus as something of a gadfly [4]. They consider Dreyfus' polemic against AI research to be at least naive, if not irresponsible, and any attempt to respond to it would only lend credence to his unfounded arguments. On the other side there are those who view Dreyfus as the leading proponent of a new philosophy of cognitive science. Excerpts from M i n d over M a c h i n e have begun to appear in trade journals and the popular press [3]. M i n d over M a c h i n e has become required reading for some graduate courses in expert systems. In the end, either course is probably unwise. To ignore this book and Dreyfus' earlier writings on AI would be to sacrifice the unique and important perspective of an informed outside observer. However, to accept uncritically Dreyfus' jaundiced view of the field would be equally costly Dreyfus has set out to make an argument that AI doesn't work. His biased assessment of the accomphshments of workers in the field is excessively critical. Expert system research has already produced practical results. The list of existing expert systems is large and growing rapidly [5]. However, it is true that research in expert systems and in AI in general could use some direction. Dreyfus' criticisms can help us find that direction by helping us to focus on some of the critical issues for future research.
140
BOOK REVIEW REFERENCES
1 deDombal, F T , Leaper, D J , Horrocks, J C , Stanlland, J R and McCann, A P , Human and computer-aided diagnosis of abdominal pare Further report with emphasis on performance of chmclans, Brtttsh Medical J (1974) 376-380 2 Dreyfus, H L , What Computers Can't Do A Crmque of Artlficlal Reason (Harper & Row, New York, 1972) 3 Dreyfus, H and Dreyfus, S , Why expert systems do not exhibit expertise, IEEE Expert 1 (2) (1986) 86-90 4 McCorduck, P , Machmes Who Thmk A Personal Inquiry mto the History and Prospects o I Artlfictal lntelhgence (Freeman, New York, 1979) 5 Waterman, D , Understanding Expert Systems (Addxson-Wesley, Reading, MA, 1986) 6 Wlpke, W T , Ouch1, G I and Knshnan, S , SECS An apphcatlon of artificial mtelhgence techmques, Artificial lntelhgence 11 (1978) 173-193