These anecdotes may provide some sense of the obstacles facing any attempt at collaboration between AI and the humanities. In particular, they illustrate certain aspects of AI's conception of itself as a discipline. According to this self-conception, AI is a self-contained technical field. In particular, it is a practical field; to do AI is to prove theorems, write software, and build hardware whose purpose is to "solve" previously defined technical "problems." The whole test of these activities lies in "what works." The criterion of "what works" is straightforward, clear, and objective in the manner of engineering design; arguments and criticisms from outside the field can make no claim at all against it. The substance of the field consists in the "state of the art" and its history is a history of computer programs. The technical methods underlying these programs might have originated in other fields, but the real work consisted in formalizing, elaborating, implementing, and testing those ideas. Fields which do not engage in these painstaking activities, it is said, are sterile debating societies which do not possess the intellectual tools-most particularly mathematics-to do more than gesture in the general direction of an idea, as opposed to really working it out.
I will be thought to exaggerate. Technicians will protest their respect for great literature, and the attitudes I have reported will be put down to a minority of fundamentalists. Yet the historical record makes plain that interactions between AI and the humanities have been profoundly shaped by the disciplinary barriers that such attitudes both reflect and reproduce. Serious research in history and literature, for example, has had almost no influence on AI. This is not wholly due to ignorance on the part of technical people, many of whom have had genuine liberal educations. Rather AI, as a technical field, is constituted in such a way that its practitioners honestly cannot imagine what influence those fields could have.
Philosophy has had a little more influence. Research on AI's constitutive questions in the philosophy of mind is widely read and discussed among AI research people, and is sometimes included in the curriculum; but these discussions are rarely considered part of the work of AI-judging, for example, by journal citations-and any influence they might have had on the day-to-day work of AI has been subtle at best. Contemporary ideas from the philosophies of language and logic have been used as raw material for AI model-making, though, and philosophers and technical people have collaborated to some degree in specialized research on logic.
Perhaps the principal humanistic influence on AI has derived from a small number of philosophical critics of the field, most particularly Hubert Dreyfus. For Dreyfus, the project of writing "intelligent" computer programs ran afoul of the critique of rules in Wittgenstein, and of Heidegger's analysis of the present-at-hand way of relating to beings (in this case, symbolic rules).[1] The use of a rule in any practical activity, Dreyfus argues, requires a prior participation in the culturally specific form of life within which such activities take place. The attempt to fill in the missing "background knowledge" through additional rules would suffer the same problem and thus introduce a fatal regress.[2] Although most senior AI researchers of my acquaintance stoutly deny having been affected by Dreyfus's arguments, a reasonable amount of research has addressed the recurring difficulties with AI research that Dreyfus predicted. One of these is the "brittleness" of symbolic, rule-based AI systems, which derives from their tendency to fail catastrophically in situations that depart even slightly from the whole background of operating assumptions that went into the system's design. For the most part, the response of AI researchers to these difficulties, and to Dreyfus's analyses generally, is to interpret them as additions to AI's agenda that require no fundamental rethinking of its premises.[3]
Within the field itself, critical reflection is largely a prerogative of the field's most senior members, and even these papers are published separately from the narrowly technical reports, either in non-archival publications like the AI Magazine or in special issues of archival publications devoted to the founders' historical reflections. In 1990 I received a referee's report on an AAAI (American Association for Artificial Intelligence) conference paper that read in part,
In general, avoid writing these "grand old man" style papers until you've built a number of specific systems & have become a grand old man.
The boundaries of "real AI research," in short, have been policed with great determination.
Yet this is now changing. In part the current changes reflect sociological shifts in the field: in particular, its decentralization away from a few heavily funded laboratories and the resulting, albeit modest, trend toward interdisciplinary pluralism. But the change in atmosphere has also been influenced by genuine dissatisfactions with the field's original technical ideas. AI's practices of formalizing and "working out" an idea constitute a powerful method of inquiry, but precisely for this reason they are also a powerful way to force an idea's internal tensions to the surface through prolonged technical frustrations: excessive complexity, intractable inefficiency, difficulties in "scaling up" to realistic problems, and so forth. These patterns of frustration have helped clear the ground for a new conception of technical work, one that recognizes the numerous, deep continuities between AI and the humanities. Although these continuities reach into the full range of humanistic inquiry, I will restrict myself here to the following five assertions about AI and its relationship to philosophy:
1. AI ideas have their genealogical roots in philosophical ideas.
2. AI research programs attempt to work out and develop the philosophical systems they inherit.
3. AI research regularly encounters difficulties and impasses that derive from internal tensions in the underlying philosophical systems.
4. These difficulties and impasses should be embraced as particularly informative clues about the nature and consequences of the philosophical tensions that generate them.
5. Analysis of these clues must proceed outside the bounds of strictly technical research, but they can result in both new technical agendas and in revised understandings of technical research itself.
In short, AI is philosophy underneath. These propositions are not entirely original, of course, and some version of them underlies Dreyfus's early critique of the field. My own purpose here is to illustrate how they might be fashioned into a positive method of inquiry that maintains a dialogue between the philosophical and technical dimensions of AI research. To this end, I will present a brief case study of one idea's historical travels from philosophy through neurophysiology and into AI, up to 1972. Although much of this particular story has been told many times, some significant conclusions from it appear to have escaped analysis. It is an inherently difficult story to tell, since it requires a level of technical detail that may intimidate the uninitiated without nearly satisfying the demands of initiates. It is a story worth telling, though, and I will try to maintain a firm sense of the overall point throughout. I will conclude by briefly discussing recent developments that have been motivated in part by critical reevaluations of this tradition, and by sketching the shape of the new, more self-critical AI that is emerging in the wake of this experience.
...the body is regarded as a machine which, having been made by the hands of
God, is incomparably better arranged, and possesses in itself movements which
are much more admirable, than any of those which can be invented by man. ...if
there had been such machines, possessing the organs and outward form of a
monkey or some other animal without reason, we should not have had any means
of ascertaining that they were not of the same nature as those animals. On the
other hand, if there were machines which bore a resemblance to our body and
imitated our actions as far as it was morally possible to do so, we should
always have two very certain tests by which to recognize that, for all that,
they were not real men. The first is, that they could never use speech or
other signs as we do when placing our thoughts on record for the benefit of
others. For we can easily understand a machine's being constituted so that it
can utter words, and even emit some responses to action on it of a corporeal
kind, which brings about a change in its organs; for instance, if it is
touched in a particular part it may ask what we wish to say to it; if in
another part it may exclaim that it is being hurt, and so on. But it never
happens that it arranges its speech in various ways, in order to reply
appropriately to everything that may be said in its presence, as even the
lowest type of man can do. And the second difference is, that although
machines can perform certain things as well as or perhaps better than any of
us can do, they infallibly fail short in others, by the which means we may
discover that they did not act from knowledge, but only from the disposition
of their organs. For while reason is a universal instrument which can serve
for all contingencies, these organs have need of some special adaptation for
every particular action. From this it follows that it is morally impossible
that there should be sufficient diversity in any machine to allow it to act in
all the events of life in the same way as our reason causes us to act.[4]
It is worth quoting Descartes's words at such length because they contained
the seeds of a great deal of subsequent intellectual history. Distinctions
between the body and the soul were, of course, of great antiquity, as was the
idea that people could be distinguished from animals by their reasoned use of
language. Descartes, though, extended these ideas with an extremely detailed
physiology. His clearly drawn dualism held that automata, animals, and the
human body could all be explained by the same mechanistic laws of physics, and
he set about partitioning functions between body and mind.[5] In establishing this partition, one of the tests was the
conventional distinction between animal capabilities, which reside in the
body, and specifically human capabilities, which required the exercise of the
soul's faculties of reason and will. Thus, for example, automata or animals
might utter isolated words or phrases in response to specific stimuli, but
lacking the faculty of reason they could not combine these discrete units of
language in an unbounded variety of situationally appropriate patterns. The
soul itself has ideas, but it has no physical extent or structure. Thus, as
Descartes explains in The Passions of the Soul (Articles 42 and 43), memory is
a function of the brain; when the soul wishes to remember something, it causes
animal spirits to propagate to the spot in the brain where the memory is
stored, whereupon the original image is presented once again to the soul in
the same manner as a visual perception.
The attraction of Descartes's proposals lay not in their particulars, many of
which were dubious even to his contemporaries. Rather, Descartes provided a
model for a kind of theory-making that contrasted with late scholastic
philosophy in every way: it was specific and detailed, it was grounded in
empirical physiology, and it was written in plain language.
The first and most influential revival of research into mental mechanisms,
Karl Lashley's 1951 paper "The Problem of Serial Order in Behavior," did not
acknowledge any sources beyond the linguistics, psychology, and
neurophysiology of the 1940s.[7] Nonetheless, the
underlying continuities are important for the computational ideas that
followed. Despite his own complex relationship to behaviorism, Lashley's paper
argued clearly that behaviorist psychology could not adequately explain the
complexity of human behavior. Lashley focused on a particular category of
behavior, namely speech. He pointed out that linguists could demonstrate
patterns to the grammar and morphology of human languages that are hard to
account for by using the theory of "associative chains," whereby each action's
effects in the world give rise to stimuli that then trigger the next action in
turn. The formal structures exhibited by human language, then, were sufficient
reason to restore some notion of mental processing to psychology.
Moreover, Lashley suggested that all action be understood on the model of
language. He regarded both speech and physical movement as having a "syntax,"
and he sought the physiological basis of both the syntax of movement and the
choice of specific movements from among the syntactically possible
combinations. This suggestion was enormously consequential for the subsequent
development of cognitivist psychology, and particularly for AI. Lashley
summarized the idea in this way:
It is possible to designate, that is, to point to specific examples of, the
phenomena of the syntax of movement that require explanation, although those
phenomena cannot be clearly defined. A real definition would be a long step
toward solution of the problem. There are at least three sets of events to be
accounted for. First, the activation of the expressive elements (the
individual words or adaptive acts) which do not contain the temporal
relations. Second, the determining tendency, the set, or idea. This
masquerades under many names in contemporary psychology, but is, in every
case, an inference from the restriction of behavior within definite limits.
Third, the syntax of the act, which can be described as an habitual order or
mode of relating the expressive elements; a generalized pattern or schema of
integration which may be imposed upon a wide range and a wide variety of
specific acts. This is the essential problem of serial order; the existence of
generalized schemata of action which determine the sequence of specific acts,
acts which in themselves or in their associations seem to have no temporal
valence.[8]
Two things are new to cognitive theorizing here: grammar as a principle of
mental structure and the generalization of grammatical form to all action. But
a great deal in Lashley's account is continuous with that of Descartes. To
start with, it is an attempt at an architecture of cognition. Indeed, it is
considerably less detailed than Descartes's architecture, although Descartes
provided no account of the mechanics of speech. Both Lashley and Descartes
assign the ability to speak individual words-or in Lashley's case, to make
individual discrete physical movements-to individual bits of machinery,
without being very specific about what these bits of machinery are like; and
they both view the human capacity for putting these elements together as the
signature of the mind. To be sure, Lashley's argument rests on the formal
complexity of speech whereas Descartes's points at the appropriateness of each
utterance to the specific situation. In each case, though, what counts is the
capacity of the mind to order the elements of language in an unbounded variety
of ways.
The continuities go deeper. Lashley, as a neurophysiologist, shows no signs of
believing in an ontological dualism such as Descartes's. Yet the conceptual
relations among the various components of his theory are analogous to those of
Descartes. In each case, the brain subserves a repertoire of bodily
capacities, and on every occasion the mind orders these in accord with its
choices, which themselves are not explained. For Descartes, the mind's choices
simply cannot be explained in causal terms, though its operations can be
described in the normative terms of reason, as for example in his Rules for
the Direction of the Mind. Lashley does not express any overt skepticism about
his "determining tendency," but neither does he have anything very definite to
say about it; the concept stays nebulous throughout. This is not simply an
incompleteness of Lashley's paper but is inherent in its design: the purpose
of the determining tendency is not to have structure in itself but to impose
structure upon moment-to-moment activities from the repertoire of action
schemata made available to it by the brain.
In retrospect, then, Lashley's paper makes clear the shape of the challenge
that the cognitivists had set themselves. They wished to rout their sterile
behaviorist foes in the same way that Descartes had routed the schoolmen, by
providing a scientific account of cognitive processes. The problem, of course,
is that Descartes was not a thoroughgoing mechanist. So long as the
cognitivists retained the relational system of ideas that they had inherited
from Descartes, and from the much larger tradition of which Descartes is a
part, each of their models would include a component corresponding to the
soul. No matter how it might be squeezed or divided or ignored, there would
always remain one black box that seemed fully as intelligent as the person as
a whole, capable of making intelligent choices from a given range of options
on a regular basis. As the field of AI developed, this recalcitrant box
acquired several names. Dennett, for example, spoke of the need for
"discharging the homunculus," something he imagined to be possible by dividing
the intelligent homunculus into successively less intelligent pieces,
homunculi within homunculi like the layers of an onion, until one reached a
homunculus sufficiently dumb to be implemented in a bit of computer
code.[9]
AI researchers' jargon spoke of subproblems as being "AI-complete" (an
analogy: so-called NP-complete computational problems are thought to be
unsolvable except through an enumeration of possible solutions-an efficient
algorithm for any one such problem would yield efficient algorithms for all of
them). Furthermore, several exceedingly skilled programmers devised computer
systems that were capable of reasoning about their own operation, including
reasoning about their reasoning about their own operation, and so on ad
infinitum.[10] In each case, the strategy was to reduce
the soul's infinite choices to finite mechanical means.
But beyond sketching the shape of a future problem, Lashley also sketched the
principal strategy of a whole generation for solving it. The operation of the
determining tendency might be a mystery, but the general form of its
accomplishment was not. While the linguistic metaphor for action envisions an
infinite variety of possible actions, it also imposes a great deal of
structure on them. In mathematical terms the possible actions form a "space."
The generative principle of this space lies in the "schemata of action," which
are modeled on grammatical rules. A simple schema for English sentences might
be:
Sentence --> NounPhrase IntransitiveVerb.
That is, roughly speaking "one way to make a sentence is to utter a noun
phrase followed by an intransitive verb." Other rules might spell out these
various "categories" further, for example:
NounPhrase --> Article Noun
Article --> a
Article --> the
Noun --> cat
Noun --> dog
IntransitiveVerb --> slept
IntransitiveVerb --> died
These mean, roughly, "one way to make a noun phrase is to utter an article
followed by a noun," "some possible articles are a and the," "some possible
nouns are cat and dog," and "some possible intransitive verbs are slept and
died." There might be other ways to make sentences, for example:
Sentence --> NounPhrase TransitiveVerb NounPhrase
TransitiveVerb --> saw
TransitiveVerb --> ate
This particular set of grammatical rules generates a finite space of English
sentences, for example:
the cat saw a dog
a dog ate a dog
The process of "deriving" a sentence with these rules is simple and orderly.
One begins with the category Sentence, and then at each step one makes two
choices: (1) which category to "expand," and (2) which rule to apply in doing
so, until no categories are left. For example, one might proceed as
follows:
1. Sentence
2. NounPhrase TransitiveVerb NounPhrase
3. NounPhrase TransitiveVerb Article Noun
4. NounPhrase saw Article Noun
5. Article Noun saw Article Noun
6. the Noun saw Article Noun
7. the Noun saw Article dog
8. the cat saw Article dog
9. the cat saw a dog
The space of possible sentences, then, resembles a branching road with a
definite set of choices at each point. The process of choosing a sentence is
reduced to a series of much smaller choices among a small array of
alternatives. The virtue of this reduction becomes clearer once the grammar
generates an infinite array of sentences, as becomes the case when the
following grammatical rules are added to the ones above:
Sentence --> NounPhrase CognitiveVerb that Sentence
CognitiveVerb --> thought
CognitiveVerb --> forgot
It now becomes possible to generate sentences such as
the cat thought that the dog forgot that a cat slept
Chomsky in particular made a great deal of this point; following Humboldt, he
spoke of language as making "infinite use of finite means."[11] Further, although he believes that the mind ultimately
has a biological (and thus mechanical) explanation, he has focused his
research on the level of grammatical competence rather than trying to uncover
this explanation himself.[12]
Newell and Simon placed enormous significance on this idea, and justifiably
so.[14] While maintaining the system of conceptual
relations already found in Descartes, Lashley, and Chomsky, their program
nonetheless embodied a serious proposal for the mechanization of the soul.[15] Their strategy was ingenious: rather than endow the
soul with an internal architecture-something incomprehensible within the
system of ideas they inherited-they effectively proposed interpreting the soul
as an epiphenomenon. Ironically, given Descartes's polemics against
scholastic philosophy, the idea is approximately Aristotelian: the soul as the
form of the person, not a discrete component. More specifically, rather than
being identified with any particular device, the soul was contained by the
generative structure of the search space and manifested through the operation
of search mechanisms. These search mechanisms were "heuristic" in the sense
that no single choice was ever guaranteed to be correct, yet the overall
effect of sustained searching was the eventual discovery of a correct outcome.
Despite the simplicity and limitations of their early programs, Newell and
Simon were willing to refer to these programs' behavior as "intelligent"
because they met this criterion. In addition, they regarded their proposal as
promising because so many human activities could readily be cast as search
problems.
Up to this point, the story of the mechanization of the soul is a conventional
chapter in the history of ideas: to tell this story, we trace the unfolding of
an intellectual project within an invariant framework of continuities or
analogies among idea-systems. With Newell and Simon's program, though, the
story clearly changes its character. But how exactly? So far as the
disciplinary culture of AI is concerned, the formalization and implementation
of an idea bring a wholly new day-a discontinuity between the prehistory of
(mere) questions and ideas and the history, properly speaking, of problems and
techniques. Once this "proper" history has begun, technical people can put
their proposals to the test of implementation: either it works or it does not
work.
Yet despite this conception, and indeed partly because of it, the development
of technical methods can be seen to continue a long trajectory largely
determined by the defining projects and internal tensions of the ancestral
systems of ideas. In particular, these projects and tensions continue to
manifest themselves in the goals and tribulations of AI's technical work. In
the case of Newell and Simon's proposal, the central goals and tribulations
clustered around the "problem" of search control: that is, making heuristic
search choices well enough-not perfectly, just well enough-to allow the search
process to "terminate" with an acceptable answer within an acceptable amount
of time. An enormous AI subliterature addresses this problem in a wide variety
of ways. Within this literature, searches are said to "explode" because of the
vastness of search spaces. It should be emphasized that mediocre search
control ideas do not kill a mechanism; they only slow it down. Yet this
research has long faced a troubling aporia: the more complicated the world is,
the more choices become possible at each point in the search, and the more
ingenuity is required to keep the search process under control. The metaphors
speak of a struggle of containment between explosion and control. Such a
struggle, indeed, seems inherent in any theory for which action is said to
result from formal reason conducted by a finite being.[16]
Newell and Simon's achievement thus proved tenuous. So long as AI's
self-conception as a self-sufficient technical discipline has remained intact,
however, these difficulties are readily parsed as technical problems seeking
technical solutions. An endless variety of solutions to the search control
problem has indeed been proposed, and each of them more or less "works" within
the bounds of one or another set of "assumptions" about the world of practical
activity.
To those who have had experience getting complex symbolic programs to work,
the STRIPS papers make intense reading. Because the authors were drawing
together so many software techniques for the first time, the technically
empathetic reader gets a vivid sense of struggle: the unfolding logic of what
the authors unexpectedly felt compelled to do, given what seemed to be
required to get the program to work. A detailed consideration of the issues
would take us much too far afield, but the bottom line is easy enough to
explain. As might be expected, this bottom line concerns the technical
practicalities of search control. A great deal is at stake: if the search can
be controlled without making absurdly unrealistic assumptions about the
robot's world, then the program can truly be labeled "intelligent" in some
non-trivial sense.
Consider, though, what this search entails. The STRIPS program is searching
for a correct plan: that is, a plan which, if executed in the world as it
currently stands, would achieve a given goal. This condition-achieving a given
goal-is not simply a property of the plan; it is a property of the robot's
interactions with its world. In order to determine whether a given plan is
correct, then, the program must effectively conduct a simulation of the likely
outcome of each action. For example, if a candidate plan contains the
primitive action "step forward," it matters whether the robot is facing a
wall, a door, a pile of rubbish, or an open stretch of floor. If "step
forward" is the first step in the plan, then the robot can predict its outcome
simply by activating its video camera and looking ahead of itself. But if
"step forward" is the seventh step in the plan, subsequent to several other
movements, then complex reasoning will be required to determine its likely
outcome.
This is a severely challenging problem, and Fikes and Nilsson approached it,
reasonably enough given the state of computer technology in 1971, through
brute force: they encoded the robot's world in the form of a set of formulae
in the predicate calculus, and they incorporated into STRIPS a general-purpose
program for proving predicate-calculus theorems by means of a search through
the space of possible formal proofs. This approach "works" in the same sense
that any search method works: if the search ever terminates, then the answer
is correct, but how long this takes depends heavily on the perspicacity of the
program's search control policies. And adequately perspicuous search control
policies are notoriously elusive. As programmers like Fikes and Nilsson
quickly learned, the trick is to design the world, and the robot's
representations of the world, in such a way that long, involved chains of
reasoning are not required to predict the outcomes of actions.
Yet predicting the outcomes of actions was, as programmers say, only the
"inner loop" of the plan-construction process. Recall that the overall process
of choosing possible actions is also structured as a search problem; extending
Lashley's linguistic metaphor, it is as if the grammaticality of a spoken
sentence depended on the listener's reaction to each successive word.
Moreover, the space of possible plans is enormous: at any given time, the
robot can take any of about a dozen primitive actions, depending on its
immediate circumstances, and even a simple plan will have several steps. Once
again, search control policies are crucial. At each point in the search
process, the program must make two relatively constrained choices among a
manageable list of options: it must choose a partially specified plan to
further refine, and it must choose a means of further refining it-roughly
speaking, it must add another primitive action to the plan.
As with any search, making these choices correctly every time would require
"intelligence" that no mechanism could probably possess. The point, instead,
is to make the choices correctly often enough for the search to settle on a
correct answer in a reasonable amount of time. This, once again, is the appeal
of heuristic search: intelligent action emerges from a mass of readily
mechanizable decisions. In other words, the problem for Fikes and Nilsson was
that they had to write bits of code whose outcomes approximated two hopelessly
uncomputable notions: "partially specified plan most likely to lead to a
correct plan" and "best primitive action to add to this subplan." Their
solution to these problems was unsurprising in technical retrospect, and the
details do not matter here. Briefly, they chose whatever partially specified
plan seemed to have gotten the furthest toward the goal with the smallest
number of primitive actions, and they chose a new primitive action that
allowed the theorem-proving program to make further progress toward proving
that the goal had been achieved. Both of these criteria are virtually
guaranteed to lead the plan-construction process down blind alleys (such as
telling the robot to head for the door before getting the key). The important
point is that these blind alleys did not hurt the robot; they only kept the
robot waiting longer to be given a plan to execute.
How big a step was the STRIPS program toward mechanized intelligence?
Reasonable people could disagree. It is certainly an impressive thing to watch
such a program in operation-provided you have long enough to wait. But the
question of search control was daunting. To the AI research people of that
era, search control in STRIPS-like plan-construction programs was a "problem"
to be addressed through a wide variety of technical means. Yet this approach
accepts as given the underlying structure of the situation: a steep trade-off
between the complexity of the world and the practicality of the search control
problem. If the robot can perform many possible actions, or if the results of
these actions depend in complex ways on the circumstances, then the search
space grows rapidly-in mathematical terms, exponentially-in size. If it is
impossible to predict the outcomes of actions-say, because the robot is not
the only source of change in the world-then the search space will have to
include all of the possible outcomes as well. In a prescient aside in the
sequel to the original STRIPS paper, Fikes, Hart, and Nilsson pointed this
out:
One of the novel elements introduced into artificial intelligence research by
work on robots is the study of execution strategies and how they interact with
planning activities. Since robot plans must ultimately be executed in the real
world by a mechanical device, as opposed to being carried out in a
mathematical space or by a simulator, consideration must be given by the
executor to the possibility that operations in the plan may not accomplish
what they were intended to, that data obtained from sensory devices may be
inaccurate, and that mechanical tolerances may introduce errors as the plan is
executed.
Many of these problems of plan execution would disappear if our system
generated a whole new plan after each execution step. Obviously, such a
strategy would be too costly, so we instead seek a plan execution scheme with
the following properties:
1. When new information obtained during plan execution implies that some
remaining portion of the plan need not be executed, the executor should
recognize such information and omit the unneeded plan steps.
2. When execution of some portion of the plan fails to achieve the intended
results, the executor should recognize the failure and either direct
reexecution of some portion of the plan or, as a default, call for a
replanning activity.[19]
Thus, although they recognized the tension that was inherent in the system of
concepts they had inherited, the technical imagination of that time provided
Fikes, Hart, and Nilsson with no other way of structuring the basic question
of intelligent action. It was fifteen years before the inherent dilemma of
plan-construction was given definite mathematical form, first by Chapman and
then more compactly by McAllester and Rosenblitt.[20]
This kind of research does not decisively discredit the conceptual framework
of planning-as-search; rather, it clarifies the precise nature of the
trade-offs generated by that framework. Indeed, productive research continues
to this day into the formal structure of plan-construction search problems.
This impasse, however, is not a failure. To the contrary, tracing the precise
shape of the impasse allows us to delineate with particular confidence the
internal tensions in the relational system of ideas around the Cartesian soul.
According to this hypothesis, the fundamental embarrassment of Descartes's
theory does not lie in the untenability of ontological dualism. Rather, it
lies in the soul's causal distance from the world of practical action. As this
world grows more complex (or, more precisely, as one's representational
schemes reflect this world's complexity more fully), and as one becomes more
fully immersed in that world, the soul's job becomes astronomically difficult.
Yet Descartes performed his analysis of the soul in sedentary conditions:
introspecting, visualizing, and isolating particular episodes of perception.
When he did discuss complex activities, he focused not on the practicalities
of their organization but on the struggles they engendered between the body
and the soul.[21]
In order to impose intelligent order on its body's actions, the Cartesian soul
faces a stern task. For example, to visualize a future course of events, the
soul must stimulate the brain to assemble the necessary elements of memory.
The reasoning which guides this visualization process must be based in turn
upon certain knowledge of the world, obtained through the senses: enough
information to visualize fully the outcomes of the individual's planned
sequence of actions. Our judgment that such a scheme places an excessive
burden on the soul-or, as technical people would say, makes the soul into a
"bottleneck"-is not a logical refutation; it is only an engineer's embodied
judgment of the implausibility of a design. But within the logic of
Descartes's project that is a lot.
The underlying difficulty takes perhaps its clearest form in Lashley. At the
beginning of his lecture, he opposes his own view to the behaviorist and
reflexological tale of stimuli and responses as follows:
My principal thesis today will be that the input is never into a quiescent or
static system, but always into a system which is already actively excited and
organized. In the intact organism, behavior is the result of interaction of
this background of excitation with input from any designated stimulus. Only
when we can state the general characteristics of this background of
excitation, can we understand the effects of a given input.[22]
In contradistinction to a scheme that focuses upon the effects of an isolated
stimulus, Lashley proposes giving due weight both to a stimulus and to the
ongoing flux of brain activity into which the stimulus intervenes. People, in
other words, are always thinking as well as interacting with the world. Having
said this, though, he immediately gives priority to the internal "background"
of neural activity, and his paper never returns to any consideration of
external stimuli and their effects. As with his silence about the nature of
the determining tendency, this is not a simple omission but rather is
intrinsic to his relational system of concepts. His analysis of action on the
model of speech portrays speakers as laying out a complex series of sounds
through internal processing and then producing them in a serially ordered
sequence, without in any way interacting with the outside world.[23] As we have already seen in the case of STRIPS, this
obscurity about the relationship between "planning" (of action sequences) and
"interaction" (with the world while those actions are going on) structured
cognitive theorizing about action, and AI research in particular, for many
years afterward.[24]
It is precisely this pattern of difficulty that has impelled an emerging
interdisciplinary movement of computational modelers to seek a conception of
intelligent behavior whose focal point is the fullness of embodied activity,
not the reticence of thought. An organizing theme of this movement is the
principled characterization of interactions between agents and their
environments, and the use of such characterizations to guide design and
explanation. When the "agents" in question are robots, this theme opens out
onto a systems view of robotic activity within the larger dynamics of the
robot's world. When the "agents" are animals, it opens out onto biology, and
specifically onto a conception of ethology in which creatures and their
behavior appear thoroughly adapted to the dynamics of a larger ecosystem. When
the "agents" in question are people, it opens out onto philosophical and
anthropological conceptions of human beings as profoundly embedded in their
social environments. In lieu of detailed references to these directions of
research, allow me to direct the reader to an issue of Artificial Intelligence
on computational theories of interaction and agency that will appear in
1995.
Putting this mode of cooperative work into practice will not be easy. The
obstacles are many and varied, but I believe that the most fundamental ones
pertain to the use in AI of mathematics and mathematical formalization. This
is not the place for a general treatment of these topics, but it is possible
at least to outline some of the issues. The most obvious issue, perhaps, is
the symbolic meaning attached to mathematics in the discursive construction of
technical disciplines. Technical people frequently speak of mathematics as
"clean" and "precise," as opposed to the "messy" and "vague" nature of the
social world and humanistic disciplines. These metaphors clearly provide rich
points of entry for critical research, but the important point here is that
their practical uses go beyond the simple construction of hierarchies among
disciplines. Most particularly, the notion of mathematics as the telos of
reason structures AI researchers' awareness in profound ways.
To see this, let us briefly consider the role that mathematics plays in AI
research. The business of AI is to build computer programs whose operation can
be narrated in language that is normally used to describe human activities.[25] Since the function of computers is specified in terms
of discrete mathematics, the daily work of AI includes the complex and subtle
discursive practice of talking about human activities in ways that assimilate
them to mathematical structures.[26] In the case of the
computational models of action described above, this assimilation is achieved
by means of a linguistic metaphor for action. This metaphor is not specific to
AI; in fact, it structures a great deal of the practice of applied
computing.[27] This fact in turn points to an inherent
source of intellectual conservatism in AI: the field is not restricted a
priori to speaking of human beings in particular terms, but it is restricted
to speaking in terms that someone knows how to assimilate to mathematical
structures that can be programmed on computers. In this way, the existing
intellectual infrastructure of computing-its stock of discursive forms and
technical methods-drags like an anchor behind any project that would reinvent
AI using language drawn from alternative conceptions of human beings and their
lives.
This observation goes far toward explaining the strange appearance that AI
presents to fields such as literature and anthropology that routinely employ
much more sophisticated and critically reflective conceptions of human life.
The first priority for AI research is to get something working on a computer,
and the field does not reward gnawing doubts about whether the conceptions of
human life being formalized along the way are sufficiently subtle, accurate,
or socially responsible-thus the emphasis, mentioned at the outset, on "doing"
as opposed to "just talking." Critical methods from the humanities are likely
to appear pointless, inasmuch as they do not immediately deliver
formalizations or otherwise explain what programs one might write. AI people
see formalization as a trajectory with an endpoint, in which the vagueness and
ambiguity of ordinary language are repaired through mathematical definition,
and they are not greatly concerned with the semantic violence that might be
done to that language in the process of formal definition. A word like
"action" might present real challenges to a philosophical project that aims to
respect ordinary usage,[28] but the assimilation of
action to formal language theory reduces the word to a much simpler form: a
repertoire of possible "actions" assembled from a discrete, finite vocabulary
of "expressive elements" or "primitives." Having thus taken its place in the
technical vocabulary of AI, the word's original semantic ramifications are
lost as potential resources for AI work. The ideology surrounding
formalization accords no intrinsic value to these left-over materials. As a
result, formalization becomes a highly organized form of social forgetting-and
not only of the semantics of words, but of their historicity as well. This is
why the historical provenance and intellectual development of AI's underlying
ideas claim so little interest among the field's practitioners.
What would a reformed AI look like? It would certainly not reject or replace
mathematics. Rather, it would draw upon critical research to cultivate a
reflexive awareness of how mathematical formalization is used as part of the
engineer's embodied work of building things and seeing what they do. In
particular, it would cultivate an awareness of the cycle of formalization, the
technical working-out, the emergence of technical impasses, the critical work
of diagnosing an impasse as reflecting either a superficial or a profound
difficulty with the underlying conception of action, and the initiation of new
and more informed rounds of formal modeling. The privilege in this cycle does
not lie with the formalization process, nor does it lie with the critical
diagnosis of technical impasses. Rather, it lies with the cycle itself, in the
researcher's "reflective conversation with the materials" of technical and
critical work.[29]
Humanistic critical practice can take up numerous relationships, cooperative
or not, to this cycle of research. My own analysis in this paper has employed
a relatively old-fashioned set of humanistic methods from the history of
ideas, tracing the continuity of certain themes across a series of authors and
their intellectual projects. Since formalization is a fundamentally
metaphorical process, discursively interrelating one set of things with
another, mathematical set, it can be particularly fruitful to trace the
historical travels of a given metaphor among various institutional sites in
society, technical and otherwise.[30] The purpose in
doing so is not simply to debunk any claims that technical institutions might
make to an ahistorical authority, but to prevent the passage to formalism from
forgetting the underlying commitments that a given way of speaking about human
activities draws from its broader cultural embedding.[31] This contextual awareness will be crucial when the
technical research reaches an impasse and needs to be diagnosed as a
manifestation of internal tensions within the underlying system of ideas. Any
given set of ideas will be more easily given up when they are seen as simply
one path among many others not taken. Indeed, this awareness of context will
be crucial for recognizing that an impasse may have occurred in the first
place. Viewed in this way, technical impasses are a form of social
remembering, moments when a particular discursive form deconstructs itself and
makes its internal tensions intelligible to anyone who is critically equipped
to hear them.
The cycle of reaching and interpreting technical impasses, moving back and
forth between technical design and critical inquiry, can be practiced on a
variety of scales, depending upon the acuity of one's critical methods. The
example I traced in the body of this paper was extremely coarse: whole decades
of research could be seen in hindsight to have been working through a single,
clear-cut intellectual problem. The difficulty was not that AI practitioners
were insulated from the philosophical critiques of Cartesian reason that might
have provided a diagnosis of their difficulties and defined the contours of
alternative territories of research. To the contrary, Hubert Dreyfus was
articulating some of these critiques all along. The real difficulty was that
the critical apparatus of the field did not provide its practitioners with a
living, day-to-day appreciation for the contingent nature of their formalisms.
Although they viewed formalization as conferring upon language a cleanliness
and precision that it did not otherwise possess, the effect was precisely the
reverse. Lacking a conscious awareness of the immense historicity of their
language, they could not understand it as it called out to them the very
things they had discovered. A reformed technical practice would employ the
tools of critical inquiry to engage in a richer and more animated conversation
with the world.
2 For a detailed analysis of this argument see Elizabeth
F. Preston, "Heidegger and Artificial Intelligence," Philosophy and
Phenomenological Research, 53.1 (1993) 43-6.
3 Dreyfus, in joint work with Stuart Dreyfus, has been
cautiously supportive of one alternative AI research program, the
"connectionist" attempt to build simulations of neural circuitry without
necessarily formulating "knowledge" in terms of symbolic "rules." See Hubert
L. Dreyfus and Stuart Dreyfus, "Making a Mind vs. Modeling the Brain: AI Back
at a Branchpoint," Daedalus, 117.1 (1988) 15-43. But as the Dreyfuses
point out, this research program still faces a long, difficult learning curve
and will not be discussed here.
4 Ren Descartes, The Philosophical Works of René
Descartes, trans. Elizabeth S. Haldane and George R. T. Ross, vol. 1
(Cambridge, UK: Cambridge UP, 1972) 116.
5 The terms "mechanism" and "mechanistic" require more
analysis than space permits here. Suffice it to say that a mechanism is a
physical object whose workings are wholly explicable in causal terms. To speak
of something as a mechanism, furthermore, is to insert it into a rhetoric of
engineering design, whether divine or human, and whether on the model of the
clockmaker or the computer programmer. For the modern mathematical
intepretations of the term, which are obviously relevant to the foundations of
computing if not immediately to the genealogy being traced here, see Judson
Chambers Webb, Mechanism, Mentalism, and Mathematics: An Essay on Finitism
(Dordrecht: Reidel, 1980). Note also that the intellectual culture of
Descartes's day did not distinguish between "mind" and "soul," and the two
terms continue to be used interchangeably in Catholic philosophy; see, for
example, Ludger Holscher, The Reality of the Mind: Augustine's
Philosophical Arguments for the the Human Soul as a Spiritual Substance
(London: Routledge, 1986). Even in the present day, these terms are
usually not so much opposed as simply employed in different discourses with
overlapping genealogies.
6 Noam Chomsky, Problems of Knowledge and Freedom: The
Russell Lectures (New York: Pantheon, 1971) 50; George A. Miller,
"Information and Memory," Scientific American, 195.2 (1956) 42-46.
7 Karl S. Lashley, "The Problem of Serial Order in
Behavior," Cerebral Mechanisms in Behavior: The Hixon Symposium, ed.
Lloyd A. Jeffress (New York: Wiley, 1951).
8 Lashley, 122.
9 Daniel Dennett, "Why the Law of Effect Will Not Go Away,"
Brainstorms: Philosophical Essays on Mind and Psychology, ed. Daniel
Dennett (Montgomery, VT: Bradford Books, 1978) 80-81.
10 Cf. Brian C. Smith, "Prologue to Reflection and
Semantics in a Procedural Language," Readings in Knowledge
Representation, ed. Ronald J. Brachman and Hector J. Levesque (Los Altos,
CA: Morgan Kaufmann, 1985).
11 Noam Chomsky, Aspects of the Theory of Syntax
(Cambridge, MA: MIT Press, 1965) 8.
12 Noam Chomsky, Language and Responsibility,
trans. (from the French) John Viertel (New York: Pantheon, 1979) 66, 97.
13 Allen Newell and Herbert A. Simon, "GPS: A Program
That Simulates Human Thought," Computers and Thought, ed. Edward A.
Feigenbaum and Julian Feldman (New York: McGraw, 1963).
14 See, for example, Newell's comments in Philip E. Agre,
"Interview with Allen Newell," Artificial Intelligence, 59.1-2 (1993)
415-449, 418.
15 C.R. Gallistel, The Organization of Action: A New
Synthesis (Hillsdale, NJ: Erlbaum, 1980) 6-7.
16 Christopher Cherniak, Minimal Rationality
(Cambridge, MA: MIT Press, 1986).
17 Richard E. Fikes and Nils J. Nilsson, "STRIPS: A New
Approach to the Application of Theorem Proving to Problem Solving,"
Artificial Intelligence, 2.3 (1971) 189-208.
18 David Chapman, "Planning for Conjunctive Goals,"
Artificial Intelligence,32.3 (1987), presents a genealogy of the AI
"planning" systems in this lineage.
19 Richard E. Fikes, Peter E. Hart, and Nils J. Nilsson,
"Learning and Executing Generalized Robot Plans," Artificial
Intelligence, 3.4 (1972) 251-288, 268.
20 Chapman; David McAllester and David Rosenblitt,
"Systematic Nonlinear Planning," Proceedings of the National Conference on
Artificial Intelligence (Los Altos, CA: Kaufmann, 1991) 634-639.
21 See, for example, Descartes, Passions of the
Soul, Article 47.
22 Lashley, 112.
23 Actual human speakers frequently do interact with
their addressees and others during the real-time production of their
utterances, but this fact is rarely taken into account in cognitive theories
of grammar and speech. See Charles Goodwin, Conversational Organization:
Interaction Between Speakers and Hearers (New York: Academic, 1981).
24 It is particularly clear in the opening chapter of
Miller, Galanter, and Pribram's influential book Plans and the Structure of
Behavior (New York: Holt, 1960).
25 Harry M. Collins, Artificial Experts: Social
Knowledge and Intelligent Machines (Cambridge, MA: MIT Press, 1990).
26 See Philip E. Agre, "Surveillance and Capture: Two
Models of Privacy," The Information Society, 10.2 (1994) 101-127. This
is obviously an attribute that AI shares with a wide variety of other fields,
for example mathematical economics, and much of the analysis here applies to
these other fields as well. It should be noted that AI people themselves place
great emphasis on a distinction between "neat" forms of AI, which openly avow
their commitment to mathematical formalization and employ large amounts of
mathematical notation in their papers, and "scruffy" forms, which do not (Cf.
Diane E. Forsythe, "Engineering Knowledge: The Construction of Knowledge in
Artificial Intelligence," Social Studies of Science, 23.3 (1993)
445-477). My argument, though, applies equally to both forms of AI research.
Regardless of whether its author was consciously thinking in terms of
mathematics, a computer program is a notation whose operational semantics can
be specified in mathematical terms. While the formalizations in "neat"
research are frequently more consistent, systematic, and explicit than those
of "scruffy" research, the design of any computer program necessarily entails
a significant level of formalization.
27 Different linguistic metaphors for human action are
obviously possible, if perhaps equally problematic; see, for example, Paul
Ricoeur, "The Model of the Text: Meaningful Action Considered as a Text,"
Social Research, 38 (1971) 529-562.
28 e.g., Alan R. White, ed., The Philosophy of
Action (London: Oxford UP, 1968).
29 Donald A. Schön, The Reflective Practitioner:
How Professionals Think in Action (New York: Basic, 1983).
30 See Emily Martin, The Woman in the Body: A Cultural
Analysis of Reproduction (Boston: Beacon, 1987); Paul McReynolds, "The
Clock Metaphor in the History of Psychology," Scientific Discovery: Case
Studies, ed. Thomas Nickles (Dordrecht: Reidel, 1980); and Philip
Mirowski, More Heat Than Light: Economics as Social Physics, Physics as
Nature's Economics (Cambridge, Uk: Cambridge UP, 1989).
31 For an impressive cultural analysis of the origins of
AI, see Paul Edwards, The Closed World: Computers and the Politics of
Discourse in Cold War America (Cambridge, MA: MIT Press, in press).
karl lashley: language as a model for action
The American cognitivists of the 1950s often modeled themselves after
Descartes, and they intended their research to have much of the same appeal.
Despite the intervening three centuries, the lines of descent are indeed
clear. This is evident in the case of Chomsky, for example, who argued in
explicitly Cartesian terms for a clear distinction between the physiology of
speech, including the biological basis of linguistic competence, and the
capacity for actually choosing what to say. While not a dualist, Chomsky
nonetheless epitomized his conception of human nature in terms of "free
creation within a system of rule." Miller used Descartes's Rules for the
Direction of the Mind to motivate his search for ways that people might more
efficiently use their limited memories.[6]allen newell and herbert simon: the mechanization of the soul
Instead, the first steps in mechanizing this idea of a generative space were
due to Newell and Simon.[13] Whereas Chomsky was
concerned simply with the precise extent of the generative space of English
grammar, Newell and Simon's computer program had to make actual choices within
a generative space. Whereas Lashley posited the existence of a "determining
tendency" whose genealogical origins lay in a non-mechanical soul, Newell and
Simon had to provide some mechanical specification of it. Here the generative
structure of the space was crucial. Newell and Simon did not employ linguistic
vocabulary. Nonetheless, just as grammatical rules and derivations provide a
simple, clear means of generating any grammatical sentence, the application of
"operators" provided Newell and Simon with a simple, easily mechanized means
of generating any possible sequence of basic actions. Choosing which sequence
of actions to adopt was a matter of "search." The mechanism that conducted the
search did not have to make correct choices all the time; it simply had to
make good enough choices eventually as it explored the space of
possibilities.richard fikes and nils nilsson: mechanizing embodied action
To watch the dynamics of this process unfold, it will help to consider one
final chapter: the STRIPS program.[17] The purpose of
STRIPS is to automatically derive "plans" for a robot to follow in
transporting objects around in a maze of rooms. The program constructs these
plans through a search process modeled on those of Newell and Simon.[18] The search space consists of partially specified plans,
with each "operator" adding another step to the plan. Returning to the
linguistic metaphor, the authors of STRIPS understand the robot's action
within a grammar of possible plans. They refer to the units of action that
Lashley called "expressive elements" as "primitive actions," and the "syntax
of the act" strings these actions into sequences that can be "executed" in the
same manner as a computer program. In Descartes's terms, the soul's faculty of
reason specifies an appropriate sequence of bodily actions, each of which may
well be complicated, its faculty of will decides to undertake them, and the
body then physically performs them.beyond the cartesian soul
The previous sections offer a critical reconstruction of a single strand of
intellectual history, a single intellectual proposition worked out in
increasingly greater technical detail such that its internal tensions become
manifest. To diagnose the resulting impasse and move beyond it, it will be
necessary to transcend AI's conception of itself as a technical, formalizing
discipline, and instead to reconsider the larger intellectual path of which AI
research has been a part. No matter how esoteric AI literature has become, and
no matter how thoroughly the intellectual origins of AI's technical methods
have been forgotten, the technical work of AI has nonetheless been engaged in
an effort to domesticate the Cartesian soul into a technical order in which it
does not belong. The problem is not that the individual operations of
Cartesian reason cannot be mechanized-they can be-but that the role assigned
to the soul in the larger architecture of cognition is untenable. This
incompatibility has shown itself in a pervasive and ever more clear pattern of
technical frustrations. The difficulty can be shoved into one area or another
through programmers' choices about architectures and representation schemes,
but it cannot be made to go away.ai and the humanities
I have argued that AI can become sterile unless it maintains a sense of its
place in the history of ideas, and in particular unless it maintains a respect
for the power of inherited systems of ideas to shape our thinking and our
research in the present day. At the same time, AI also provides a powerful
means of forcing into the open the internal structure of a system of ideas and
the internal tensions inherent in the project of getting those ideas to
"work." Thus, AI properly understood ought to be able to participate in a
constructive symbiosis with humanistic analyses of ideas.
Notes
This paper has been improved by comments from Harry Collins, GŸven
GŸzeldere, Scott Mainwaring, Beth Preston, and Jozsef Toth.
1 See Hubert L. Dreyfus, What Computers Can't Do: A
Critique of Artificial Reason (New York: Harper, 1972); Martin Heidegger,
Being and Time, trans. John Macquarrie and Edward Robinson (1927; New
York: Harper, 1961); Ludwig Wittgenstein, Philosophical Investigations,
3rd edition, trans. G.E.M. Anscombe (1953; New York: Macmillan, 1968).