Roboacs & Computer-Integrated Manufacturing, Vol 4, No 3/4, pp 655-658, 1988 0736-5845/8853 00 + 0 00 Prmted m Great Britain © 1988 Pergamon Press plc

• Paper

E A S I N G T H E T W O - W A Y T R A N S F E R O F T E C H N O L O G Y

J. H A T V A N Y 't'

Computer and Automataon Institute, Hungarian Academy of Sciences, POB 63, Budapest H-1502, Hungary

This paper discusses the use of artificial intelligence technologies in designing, implementing, operating and maintaining CIM systems.

THE PROBLEM The mechanical technology components (machining, workpiece transfer and manipulation, assembly, sur- face treatment, etc.) of CIM systems stem from one culture, with its traditionally established modes of communication, implements, arts and sciences. The computer components (operating systems, data- bases, real-time systems, scene processors, informa- tion interchange schemes, etc.) are the progeny of a different culture with its own languages and idioms, and a wholly different set of implements, arts and sciences. Communication between the two thus requires the establishment of efficient modes of information transfer between two, mutually alien, cultures. The content of this information is tech- nology, hence it is appropriate to speak of a special and very important case of technology transfer.

There is a widespread belief that the only appro- priate mode of communication between the mechan- ical and the computer sides of CIM should be one based on a unidirectional flow via an unequivocal mathematical model of the mechanical process, which can then be formulated into a computer- readable and executable algorithm. This approach has, however, a number of flaws. One of these is that engineering/manufacturing knowledge and exper- tise comprise a much broader domain than that which has hitherto been modelled, and indeed than is amenable to mathematical modelling in the fore- seeable future. (And this makes the unmodelled arts and their implements no less valuable and valid than the ones for which an appropriate mathematical idiom and tool has been found.) Secondly, we are increasingly using mathematical apparatus (proba- bilistic, fuzzy, differential topology, etc.) which is itself very difficult to couch in algorithmic terms and,

even if this can be done, requires inappropriately large computing resources for obtaining solutions. Finally, even if we are able to set up a mathematical model and it is possible to formulate an executable algorithm of reasonable size, the model and its algorithm absolutely require practical verification.

Contrary to a certain professional minority, this author does not believe in the feasibility of writing large programs for the control of complex real-time systems purely on the basis of speculative foresight and subsequent simulation. The only acceptable validation of the models on which such programs rest and of their correct formulation into algorithms, and hence reliable software, is through practical testing. In the case of a complex system like a CIM "wheel", with its vast and dynamically changing cobweb of interactions between components, this is certainly so. The fact that a CIM system is by defini- tion a flexible one, i.e. that it frequently changes its own structure, functions and goals, moreover that these systems are also exogenously frequently upgraded and changed during their life-cycle, renders the existence of permanent feedback arrangements imperative.

The unidirectional sequence of technology trans- fer shown on the left-hand side of Fig. 1 is therefore not adequate. We also need the reverse chain, shown on the right-hand side of Fig. 1 which will ultimately lead to the verification and even the on-going enhancement of existing assumptions, knowledge, models, algorithms and software.

PROPOSALS FOR SOLUTIONS The problem we have to solve is, as we have seen,

that of a two-way transfer of technology between the two cultures outlined above. As is so often the case

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656 Robotics and Computer-Integrated Manufacturing • Volume 4, Number 3/4, 1988

Engineering technology For humans

Dicttonartes Human Human

Mathematical model

C- A evaluation Syntax rules ALgorithm

CIM software Productton process Readers (for

Manufacturing Learning idioms )

AI versions

Conceptual modeLLing

RuLe - based systems

Parsing, context sensitive pattern recognition

Fig 1 The two-way transfer. Fig. 2. Overcoming the language bamer.

in matters of science and technology, an excursion into an analogous field of human relations, history and sociology, might well prove to be a fruitful source of ideas. In the case of human-to-human or nation-to-nation transfers, about which a huge body of literature is available, the most important means of communication we have is that of language. In order to overcome the mutual language barrier we use dictionaries, collections of syntax rules, readers which illustrate the usage of idioms (the occurence of patterns) in various contexts and highlight excep- tions. It is also usual to avail oneself of the services of cross cultural interpreters.

One of the great unsolved problems in this field of activity, however, is that of developing suitable atti- tudes. The most important tenet is to avoid creating even the faintest impression of superiority or con- descension on the part of either partner.

Artificial intelligence research has, as Fig. 2 shows, already developed rudimentary versions of the equivalents of most of the above language- related tools.

Frame-based conceptual modelling 1 is a means for establishing "semantic" correspondences between the related concepts of the two cultures. In more prosaic terms, it is a hopeful tool for creating, with a minimum of the "traditional" algorithmic pro- gramming, the computer-based product model which comprises not only the geometrical information about a product, but also all the technical and mana- gerial data needed to prepare for its manufacture, to make it, measure it, sell it, service it, etc.

Rule-based systems 2 help engineers to communi- cate that part of their procedural knowledge about the manufacturing process which would be very difficult or indeed impossible to formulate algo- rithmically, partly because no mathematical models are available and partly because what is to be done in a given manufacturing situation is often highly context-dependent--the context in this case being the total environment of the operation. The rules that are formulated at the same time create a syntac-

tic order which can be readily accessed by the com- puter and used to elicit further information (e.g. goal priorities and decision criteria) which the engineer has often taken for granted.

Parsing, context-sensitive pattern recognition and the whole emerging armory of what are (perhaps not quite appropriately) called "natural language tech- niques ''3 are our AI "readers". They are the means by which the specific, environment-related idioms of the engineer's trade are given unequivocal meaning in the computer's understanding (whereby sinking a hole in metal-working is distinguished from, say, sinking a sono-buoy in marine engineering).

Nevertheless the greatest hope offered by AI technology towards the development of reliable and fluent "cross cultural interpreters" lies in the field which we have designated as the attitude of the communicating partners. The author sees no reason to retract his forecast of 1980: "Software production as we today know it, i.e. the interpretation of human specifications into terms which a machine can under- stand and execute, will become a task for machines. Human beings will describe their knowledge of how a manufacturing plant will operate and communicate it to computers in natural or problem-oriented (e.g. engineering jargon) terms . . . these [software pro- ducts] will be called (engineering science / and (manufacturing technology). ''4 Perhaps the biggest step in this direction has been the introduction and wide-spread use of object-oriented and symbolic languages. 5 Unlike standard programming tech- niques, object-oriented programs are not divided into procedures and data. Processing is triggered when a message is sent to an object. The message specifies the method to be carried out and any parameters the method requires. New objects can be defined easily. Based on these new facilities, entire new schools of essential non-algorithmic pro- gramming with extremely comfortable user inter- faces have emerged (Smalltalk, Macintosh, Win- dows, etc.).

In parallel with the development and widespread

Two-way transfer of technology • J. HATVANY 657

marketing of tools for the new style of transfer made possible by new hardware and software techniques, a science of software psychology has progressed from a margina~ discipline to one with a large body of published experimental and theoretical work s (the cited paper carries 140 references). From the engineer's point of view, the most striking feature of the new developments lies in the establishment of a style of computer usage based on "improving as you proceed". Essentially, this style consists of communicating current knowledge to the computer and letting iterative intercommunication with the user and the physical system be responsible for successively improving the product model. This approach is far more easily accessible to the mechan- ical technologist's current idiom, than one which requires faultless a priori planning and algorithmic formulation as required by earlier computer lan- guages.

EXPLORATORY WORK Exploratory work at the author's institute towards

specifying, creating and testing the (mainly AI) tools required to facilitate the two-way transfer of tech- nology in the factory environment has been con- ducted primarily in three areas: 1. The use of frame-based and natural language

interface techniques to facilitate communication between machine designers and CAD systems;

2. The use of rule-based techniques to solve realistic manufacturing problems;

3. The establishment of taxonomies of problem domains, characteristic task features, generic tasks, characteristics of the required tools and of the available and emerging tools to satisfy the requirements, together with a suggested method- ology for engineering-level tool selection and sys- tems synthesis.

It will readily be appreciated that each of these prongs of investigation covers one or more of the inter-technology transfer methods discussed in the previous section. Here they will only be discussed briefly, since each has been expounded in some detail in the references cited below.

1. Theoretical investigations 7 into the possibility of creating a generic, multiparadigm interface for intelligent CAD systems in the mechanical engineer- ing field have led to a specification of a system built primarily on frame-based conceptual modelling and limited natural language (including graphics) inter- faces. This specification has been partially tested on the example of a gear-drive design and appears to be an adequate tool for CAD systems relying on pro- duct models. The work is being continued towards a

full implementation and to expansion into such bor- der areas as animated drawings for kinematics, hooks to standard graphical systems, etc.

2. In order to explore the possibilities and prob- lems of rule-based systems in a realistic factory environment, the Budapest team undertook the development of a CAD package for the design of milling machine fixtures from a modular kit. 8 Even- tually this "package" evolved into a full-scale knowledge-based system. In a real-world manu- facturing environment, the a priori choice of the heuristic search strategies usually recommended for rule-based systems (and implemented once-and- for-all in most of the commercially offered expert systems) does not give the shop floor the required freedom of maneuver. The engineer does not wish to be concerned with "heuristic search strategies"; but he does wish to have the freedom to change the precedence of the goals that are communicated to the system, i.e. to vary the goal hierarchy and see the consequences. The Budapest rule-based system allows him to do this and, where necessary, auto- matically (internally) varies the search strategies. In the extension to welding fixtures, a full integration of the analysis and detailing phases of design has been achieved, again within the philosophy of a friendly goal-variable system.

3. The first detailed report on the methodical approach to task and tool matching will be presented in August, 1987 at the 37th General Assembly of the CIRP. 9 It consists basically of two phases: • The establishment of taxonomies of tasks and of

tools, and • A proposal for methodically matching these to

meet the manufacturing engineer's momentary requirements. The four taxonomies that have been drawn up are

as follows: A: The problem domains of CIM B: The characteristic features of the engineering

tasks C: The generic tasks to which these may be reduced D: The features of the (AI, etc.) tools required E: The available and emerging computer tools.

A and B are self-explanatory. The genetic tasks selected are: C. 1. Classification:

Find the most specific place of a complex des- cription within a classification hierarchy.

C.2. State abstraction: Find the consequences of changing some characteristics of a system, e.g. after an action.

C.3. Knowledge-based information passing: Obtain some datum, using known data,

658 Robotics and Computer-Integrated Manufacturing • Volume 4, Number 3/4, 1988

defaults and inheritance relations within the system.

C.4. Object synthesis: Design an object or create a plan of action to fulfill a specification.

C.5. Hypothesis matching: Decide whether a hypothesis is valid for a situation.

C.6. Assembly of explanatory hypotheses: Using related hypotheses, explain a complex situation by creating a composite hypothesis.

Finally, here is the list of available and emerging AI (and related) tools that were considered: E.1. Expert system shells: E.I.1. Simple reasoning and explanation systems E.1.2. Text animation E.1.3. Improved spreadsheets.

E.2. Symbolic manipulation languages and pro- grams:

E.2.1. List programming--LISP E.2.2. Logic programming--PROLOG.

E.3. Knowledge representation tools: E.3.1. Frame systems E.3.2. Scripts E.3.3. Production systems (rule-based systems) E.3.4. Blackboard systems, non-hierarchical control E.3.5. Object-oriented environments E.3.6. Truth and dependency maintenance systems E.3.7. Integrated knowledge representation

environments.

E.4. Learning systems: E.4.1. Classification systems E.4.2. Inductive, self-improving programs, strat-

egies.

E.5. Interfaces: E.5.1. Restricted natural languages (for input pur-

poses) E.5.2. Restricted natural languages (for output

purposes)

E.5.3. Dialogue systems E.5.4. Other media (icons, active images, etc.).

E.6. Planning systems. The methodology recommended for the engineer

to be able to assemble his own kit o f compatible modules in order to constitute an efficient technology transfer system for the task in hand, is to proceed through the matrices relating them, simultaneously making a simple set of engineering-related decisions. It is felt that thus far the exploratory work sub- stantiates the feasibility of the two-way transfer of technology postulated in the theoretical part.

R E F E R E N C E S 1. Kimura, F., Suzuki, H.: Variational product design by

constraint propagation and satisfaction in product modelling. Ann. CIRP 35: 1, 1986.

2. Buchanan, B.G., Shortliffe, E.M.: Rule-based Expert Systems--the MYCIN Experiments of the Stanford Heuristic Programming Project. Reading, Addison- Wesley, 1984.

3. Schank, R.C., Lehnert, W.: Review of natural language processing. In Research Dtrecttons m Software Tech- nology, Wegner, P. (ed.), MIT Press, Cambridge, 1979.

4. Hatvany, J.: J~inos, J.: Software products for manu- facturing design and control. Proc. IEEE 68: 1050-1053, 1980.

5. Taylor, W.A.: Object-oriented programming: a new approach for automatic factories. CIM Rev. Summer, 1986.

6. Curtis, B. et al.: Software psychology: the need for an interdisciplinary program. Proc. IEEE 74: 1092-1106, 1986.

7. Ruttkay, Z., Allen, R.H., Laczik, B.: A multiparadigm user interface for CAD systems. 1st Eurographics Workshop on Intelligent CAD Systems. Noordwijkerhout, 1987, pp. 1-17.

8. M~irkus, A., Ruttkay, Z., V~incza, J.: Fixture design: from a CAD package to a knowledge-based system. WP E/40/87, Comp. & Aut. Inst., Budapest, 1987.

9. Mtirkus, A., Hatvany, J.: Matching AI tools to engineering requirements. Ann. CIRP 36: 1, 1987.