perm filename ROAD2.MSG[D,LES] blob
sn#154777 filedate 1975-04-12 generic text, type T, neo UTF8
∂11-APR-75 0718 network site CMUA
**** FTP mail from [A350HS02] (SIMON)
0100 AI ROAD MAP EXERCISE FOR IPTO
00200 FILE: AIPROS.A11
00300
00400 DRAFT
00500
00600 This is a rough draft of the views of Newell and Simon on where
00700 AI stands and where it is and ought to be going. It discusses
0800 briefly:
00900 1) The accomplishments of AI
01000 2) The scientific goals of AI
01100 3) The potential applications of AI
01200
01300 THE ACCOMPLISHMENTS OF AI
01400
01500 The typical form of research in AI is to build intelligent
01600 programs, capable of interesting task performances of one
01700 kind or another. The programs themselves form, of course, one
1800 of the products of the research; but the important products are the
1900 mechanisms, components of intelligence, that have been identified, and
02000 the understanding that has been reached of the characteristics
2100 these mechanisms must possess in order to support intelligent behavior.
02200 Still another product, which will not be emphasized here, is the
02300 light that has been thrown by AI research upon the mechanisms and
02400 processes of human intelligence.
2500
02600 A functional classification of the mechanisms of intelligence might
2700 place them under the following headings:
02800
02900 1) Representation and memory organization
03000 2) Problem solving
03100 3) Perception
03200 4) Language processing
3300 5) Control and processing organization
03400 6) Motor behavior
3500
03600 The category of "language processing" is not quite parallel to
3700 the others, but the topic is of sufficient importance to
03800 justify separate treatment.
3900
04000 Representation
04100
04200 The invention of list processing was one of the earliest
04300 achievements of AI research, but much subsequent research has been
04400 devoted to perfecting that invention and exploring its applications
4500 to the design of intelligent systems. Thus, the organization of
04600 semantic memories, all having list structures as their underlying
04700 mode of representation, has been one of the important areas of
4800 research progress over the past five years. We have learned how
04900 to store a vast variety of information in the form of list structures,
05000 including information derived from natural language inputs and
5100 including also discrimination nets (indexes).
05200
05300 Problem Solving
05400
05500 After the initial demonstration that a machine could be programmed
05600 to solve problems by heuristic search, some of the important subsequent
05700 developments were the programing of means-ends analysis as a central
05800 problem-solving tool, and a gradually growing understanding of how
05900 to control the direction of search (depth-first, breadth-first,
06000 and best-first search). Two broad alternative ways of representing
06100 problem situations have emerged: propositional representation with
06200 inferential search using modal logics, and modeling with search by
06300 model manipulation. In the special realm of theorem proving,
06400 much has been learned about the resolution method: its power and
06500 limitations, and the usefulness of such heuristics as unit
06600 preference and set of support.
06700
06800 Apart from the specific problem-solving systems that have been
6900 built and tested, there now exists a large body of know-how, and a
07000 much smaller body of exact mathematical theory of problem solving.
07100 Under the latter heading would be included theorems about resolution
07200 theorem proving, the alpha-beta procedure, shortest-
7300 path valuation functions, and least-search valuation functions.
07400
07500 Perception
07600
07700 An early period of exploration that emphasized very general
07800 perceptron-like systems has given way to a number of very specific
07900 systems for performing particular tasks of visual and auditory
8000 perception. Handling noisy "natural" inputs (i.e., pictorial
08100 scenes and speech) still poses formidable problems, but major
08200 progress has been made in scene analysis and in speech understanding
08300 utilizing semantic as well as phonetic clues.
08400
08500 There has been an important convergence, especially in the
08600 past five years, between work on perception and work on representation.
08700 This has been sparked by the realization that new information can
08800 only be assimilated successfully with the help of relevant information
08900 that is already stored in semantic memory. Hence, most recent
09000 work in perception (the HEURISTIC COMPILER, MERLIN, "frames")
09100 is aimed at bringing considerable contextual information to bear upon
09200 perceptual processes.
09300
09400 Control and Processing Organization
09500
09600 The first stages of AI research emphasized the exploitation of
9700 flexible list-processing languages with good general facilities
09800 for closed subroutines, recursions and generators. One
09900 important byproduct of these language features has been the
10000 formulation of the ideas of "structured programing," much of whose
10100 concepts and practices are either implicit or explicit in
0200 the programming practices and problem-solving systems of AI.
10300
10400 For the past several years, there has been considerable
10500 experimentation with new forms of program organization. Two
10600 ideas that have attracted particular attention are procedural
10700 embedding (thus blurring the program-data distinction) and the
0800 organization of AI programs as production systems.
10900
11000 Motor Behavior
11100
11200 The robot projects have thrown considerable light on the
11300 requisites for successful motor behavior in natural environments.
11400 In particular, successful perceptual-motor coordination lies at the
1500 heart of building intelligent systems that can behave appropriately
1600 in unprepared environments.
1700
11800 Language Processing
11900
12000 During the initial years of AI research, progress in natural
12100 language processing was hampered by an excessive preoccupation with
2200 syntax. During the past ten years, the situation has changed
12300 dramatically, and a great deal of understanding has been achieved of
12400 methods for using semantic information to achieve language
12500 understanding and to guide language processing.
2600
12700
12800 THE SCIENTIFIC GOALS OF AI
12900
13000 The aims of AI research are defined by the range of tasks that we
13100 would like to be able to perform, and whose performance calls
13200 for intelligence. The research agenda is defined by the distances
13300 that the systems we have built thus far fall short of
13400 the capabilities we would like them to have. We have perhaps come
13500 furthest in devising systems capable of solving relatively well-structured
13600 problems. Perceptual-motor coordination is perhaps the domain in
13700 which we have made least progress. However that may be, there are
13800 important and promising research targets along each of the main
13900 directions of research discussed in the previous section.
4000
14100 Problem solving. There are two important lines to be followed
4200 here (both of which are receiving increasing attention). One is to
4300 design systems that are capable of understanding problem instructions
14400 and of programming themselves to tackle a problem described by such
4500 instructions. The other is to design systems that are capable of
14600 operating in poorly structured problem domains: where the characteristics
14700 of problem solutions are vaguely defined, and where the problem-poser
14800 depends upon the problem solver to evoke from his semantic memory both
14900 relevant design constraints and relevant design information, ideas, and
15000 procedures without detailed instruction.
15100
15200 Representation. Clearly the research problems just mentioned
15300 are also problems in the design of representations. In addition, there
15400 is still considerable question as to what kinds of representations are
15500 most appropriate for the storage of information derived from visual
5600 displays. A major concern in the design of representations is to
15700 provide means of access to the information that is there. This
15800 concern suggests at least two research foci: matching procedures
15900 for finding structures in memory that are similar to perceived structures,
16000 and in general, the indexing of large semantic stores, whether by
16100 matching processes or otherwise.
6200 Perception. The speech-understanding projects appear to provide
16300 a useful continuing model for defining research objects in both
16400 auditory and visual perception. Robot projects, while not currently
16500 fashionable, have the useful feature of setting demanding tasks for
6600 perceptual (and especially visual) components of intelligent systems.
16700
16800 Control and Processing Organization. Our knowledge is still
16900 rudimentary on the consequences, and relative advantages and disadvantages
17000 of merging data and process representations, as against
17100 keeping them relatively distinct. Production systems show considerable
17200 promise, particularly for application to learning systems, but we
17300 still do not know much about how to order a set of productions, or to
17400 combine production systems with other, more conventional, types of
7500 program control.
17600
17700 It should be evident from these brief notes that we find it
17800 easier to define some promising directions of research than to define
17900 specific goals for that research. Traditionally, in AI research
18000 goals have been defined by specifying the behavior we expect a system
18100 to attain (geometry at the high-school level, expert chess, ability
8200 to handle language of such and such complexity, etc.). This mode of
8300 specification has perhaps been formalized most fully in defining
18400 the objectives for the speech understanding projects.
18500
18600 Specifying goals in terms of the desired capability of a system
18700 has a great deal to commend it. It makes it relatively easy to
18800 determine whether or not the goals have been attained, and it
18900 encourages movement in the direction of application (i.e., by
19000 specifying goals in terms of tasks that have real-world importance.
9100 Its main disadvantage is that it does not explicitly acknowledge
19200 the knowledge about intelligent systems that is gained even in
9300 relatively unsuccessful attempts to build such systems.
9400
19500
19600 APPLICATIONS OF AI
19700
19800 In our account of progress in AI, we limited ourselves to the
19900 basic science, and did not mention progress in application. It
20000 is nevertheless easy to list a number of significant applications,
20100 for example:
0200
20300 1) List-processing is now an important computer science software
20400 tool, and has had some effect upon hardware design as well.
0500 2) Heuristic problem-solving techniques have had a number
0600 of important applications in engineering design practice
20700 (e.g., automatic design of electrical devices), and in
0800 industrial engineering (e.g., combinatorial scheduling problems).
20900 3) Heuristic problem-solving systems have been built for analysing
21000 mass spectrogram data, for synthesizing molecules,
21100 and for automating some aspects of chemical engineering design.
21200 4) Programing languages and practices in AI have been a principal
21300 source for the ideas that went into structured programming.
1400 5) Research in automatic programing has produced a system
1500 that is at least at the threshhold of feasibility for data-base
21600 design.
21700
21800 It will be noticed from these examples (and as a comment on the
21900 earlier discussion of research goals) that conceptual advances
2000 (e.g., items 1 and 4) have been at least as important for applications
22100 as have been specific intelligent systems. In spite of this
22200 experience of the past, the recent progress that has been made
2300 (especially with respect to representation and language processing)
2400 holds out increasing promise that we may be able to develop in the
22500 next period of work a larger number of intelligent systems that
22600 perform real-world tasks at levels of competence and costs
22700 that will make genuine applicatons feasible. Most of the applications
22800 that come readily to mind will call for systems with far more
22900 semantic informtion available to them than most of the AI systems
23000 built thus far.
23100
23200
23300
23400 We will halt here, with these rough records of our thinking-aloud
23500 processes, in order to get this draft to you by the Friday noon
23600 deadline. If it is at all possible, we will transmit an elaborated
3700 draft before the Monday meeting.
3800
23900 A. Newell and H. A. Simon
∂12-APR-75 1019 network site ISI
Date: 12 APR 1975 1019-PDT
From: AMAREL at USC-ISI
Subject: CONTRIBUTION TO THE 'ROADMAP IN THE AI AREA'
To: LICKLIDER, EARNEST at SU-AI, FEIGENBAUM, NEWELL at CMU-10A,
To: NILSSON at SRI-AI, WINSTON at MIT-AI
cc: AMAREL
IN THE FOLLOWING I AM GIVING AN OUTLINE OF CURRENT SCIENTIFIC/
TECHNICAL PROBLEMS IN AI (AS I SEE THEM), AND A LIST OF AI APPLICATIONS
OF POSSIBLE SIGNIFICANCE TO DOD - THAT I BELIEVE CAN BE APPROACHED NOW.
I AM ALSO PROPOSING AN APPROACH TO APPLICATIONS-ORIENTED WORK IN THE
AI AREA, AND I AM EXPRESSING CERTAIN CONCERNS ABOUT ISSUES THAT MUST BE
ADDRESSED IN DRAWING A 'ROADMAP'.
THE MATERIAL BELOW IS IN NO WAY COMPLETE. I HOPE IT WILL BECOME
CLEARER IN OUR DISCUSSIONS OF APRIL 14 IN WASHINGTON.
----------
A. SCIENTIFIC AND TECHNICAL PROBLEMS
1. Problems of Representation.
How to represent problems of different types; how to shift
representations; how to acquire and manage knowledge within
a given representational framework; how to coordinate and
effectively use different bodies of knowledge in a domain
(e.g., systematic-scientific knowledge about a system and
also informal, experiential, knowledge about its operation;
two models of a system at different levels of resolution);
how to change stored knowledge on the basis of new data,
operational experience, or beliefs.
2. Problem-solving strategies.
(a) Derivation Problems: How to effectively generate a path
between two specified states (this is the old problem of
heuristic search, but it deserves more work); how to
form plans from operational experience and how to best
use plans; what beyond resolution in mechanical
reasoning (natural inference?).
(b) Interpretation/diagnosis problems: Given a set of data
(signals from sensors, test results, intelligence
information, etc.) find the most plausible hypothesis
about causative agents, underlying processes, chains of
events, etc., in terms of which the data can be
explained.
(c) Formation problems: synthesize a system (e.g. a
program) from given specifications, infer a theory from
a body of experience.
Problems of type (b) and (c) are closely related. They are
central to many 'real life' problem-solving situations.
However, we know much less about them than we know about
problems of type (a). In many large system applications
(e.g. the 'Underwater Listening' problem) problems of the
three types coexist. An important question is how to design
a good integrated system which handles well this variety of
problem types.
Problems of representation (1 above) and questions of
strategy are tightly interdependent. An important question
in complex AI applications is: given a variety of knowledge
in a domain and a specific task at hand - how to focus on
relevant aspects of the knowledge base to handle the task in
an effective way.
3. Systems, Languages and Implementation Methodologies.
How to facilitate communication between a domain expert and
a knowledge base; how to provide the expert - and his
computer science collaborator - with a convenient
environment for specifying, changing and testing systems.
PAGE 2
How to implement in efficient ways more powerful control
structures than are presently available.
B. APPLICATIONS
1. Interpretation of Underwater signals (the TTO problem).
2. Maintenance problems; diagnosis/prognosis of malfunctions in
specific systems (including computer systems).
3. Interpretation Aids for Intelligence Analysts (e.g.
inference of patterns of scientific/technical developments
from published material in combination with other 'side
information').
4. Selective summarization of information and recommendation of
courses of action to decision makers in situations where
response time is critical.
5. Logistics and Scheduling problems. Development of heuristic
procedures for significant OR problems (e.g. network
design, resource allocation, warehouse placement).
6. Software design from non-procedural specifications. Program
synthesis and debugging.
7. Development of a modeling facility for
scientific/engineering problems which would include a
library of numerical and symbolic manipulation packages as
well as an intelligent 'front end' which would assist a user
in the development and testing of his mathematical models.
Work with partial differential equations on turbulence or
heat transfer models would be a good initial focus.
Each of these applications involves various mixtures of the
scientific/technical problems discussed in (A) above. In each
case, the most crucial effort is the choice of a knowledge base
and of a way of representing it on the computer.
Work on applications requires close collaboration between
computer scientists and experts in the problem area. The
approach to design and implementation should be responsive to
the fact that the Knowledge base in a domain is not stationary -
usually, it is in a state of flux. Our experience at Rutgers in
AI applications to medicine and psychological modeling (in a NIH
sponsored project) shows how important it is to proceed in
system development both from 'bottom-up' and from 'top-down'. A
reasonable pattern is as follows:
(a) Specific problems in an application area are approached
directly and in depth; existing ideas and AI methods are
adapted to the given situation; where choices have to be
made between the search for general methods on the one hand
PAGE 3
and the obtaining of specific results and the building of
prototype systems on the other, the latter approach is
taken. In a second phase, generalization/improvements of
the initial approach takes place. To a great extent they
are influenced by parallel work on,
(b) general systems for flexibly acquiring, managing and using
Knowledge in the domain. This parallel work is essential
for creating sufficiently flexible and useful systems.
Each of the applications that I mentioned will provide a good
environment for work on (a large number of) the scientific and
technical problems of AI. I believe that the dominant factor in
the choice of an application is the expectation of a good,
working, collaborative arrangement between the computer
scientists in a project and experts in the application area.
The success of an application prospect depends heavily on the
dedicated participation of at least one individual expert in the
project - not only for an initial period of general orientation
and advice, but on a continuing basis.
C. APPROACH
In any application area it is essential to combine system design
and experimentation activities with relevant core work in AI. I
think that work on applications can build on substantial
progress already done in AI; conversely, I am convinced that the
challenge of 'real life' applications will invigorate AI and it
will guide it to interesting problems that could not be readily
appreciated in a completely 'sheltered' environment. On the
other hand, it is important to permit basic work (controlled
experiments, special studies, development of general methods and
tools) to grow together with the applications-oriented
activities.
Therefore, each AI group should have a combination of
applications projects and core AI projects. In addition, good
communications and collaborative ties should be established
among the groups and also between each group and various
application-oriented activities. It would help to seek closer
ties with TTO, STO and with other agencies (especially,
intelligence agencies). More work is needed now on the
identification of promising AI applications.
Our experience with AI applications at Rutgers shows that
effective collaborative developments require a fairly
symmetrical commitment between the computer scientist on the one
hand and the 'man with the problem' on the other. A
service-support relationship will not do (in either direction).
It should be the responsibility of the AI groups to seek/create
the appropriate collaborative arrangements.
PAGE 4
The ARPANET provides a good medium for real collaboration (in
program development, testing and improvement) and communication.
A program developed on an AI group's machine can be accessed and
tested via the net by the collaborating applications groups. A
tool (language utility program, etc.) developed by one AI group
can be used by another group over the net.
A series of Annual AI Applications Workshops should be
instituted with the dual purpose of technical communications
(including detailed system demonstrations) between AI groups,
and also communications with the 'potential user community'.
D. CONCERNS
AI is at the cutting edge of computer science, and AI groups in
the country have been important centers of education for young
scientists who are advancing the computer field in many ways. I
hope that a redirection of AI activities will preserve as much
as possible this important function.
If the basic aspects of AI are taken out of ARPA supported AI
projects, then it would be extremely difficult to continue
serious AI work in Universities. On the other hand, it is
possible to maintain a high level of University activity and
interest if appropriate mixtures of applications work and basic
work are supported. The detailed control of these mixtures
should be in the hands of the PI's and the senior investigators
- under general guidance from IPTO.
The problem of classified information may create difficulties in
working on AI applications in Universities. This problem may
induce the creation of a separate Institute for AI applications
- of the type advocated by Feigenbaum. The idea of such an
Institute deserves serious consideration. It could consist of a
small permanent group which would be augmented by faculty or
students coming from University AI projects and visiting for
limited periods of time (e.g., a summer, or a semester). The
question of distributing responsibilities between the Institute
and the University AI groups is not simple. It would be
inappropriate to leave all applications work in the Institute
and to restrict the Universities to 'purists only'. The problem
is how to distribute applications activities between an
Institute (where classified work can take place) and a
University group. There has been some experience with this type
of problem in the past - and it is possible that a reasonable
solution can be found in the present case.
----------
THIS IS ALL FOR NOW. SORRY FOR BEING LATE IN SENDING THIS IN.
REGARDS
SAUL AMAREL
-------