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Pertinence and utility of artificial intelligence techniques for production management systems
Jbzsef Hatoany Memorial
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Pertinence and Utility of Artificial Intelligence Techniques for Production Management Systems L. Pun Universit~ de Bordeaux L Laboratoire de Productique et d'Automatique GRAI, 351 cours de la Liberation, 33405 Talence, France Imitation of lucidity and humour were two main characteristics of J. Hatvany. The paper contains five parts. (1) Definitions of pertinence and utility. (2) Problematics of production management systems to determine intelligence need. (3) Potentiality analysis of AI techniques. (4) Utility analysis of AI techniques. (5) Suggestions of a filling rock: a situation algebra.
Keywords: AI techniques, Production management, Decision support systems, Situation algebra.
Pun received his B.E.E. and B. M a t h from Aurora University, Shanghai, and his Dipl. Ing.-ESE from Ecole Sup~trieure d'Electricit~, Paris in 1951. He earned the degree of Ing.-Dr, from the University of Grenoble in 1954 and State Dr. of Sciences from the University of Toulouse. He has worked some twenty years in Industrial D e v e l o p m e n t ( G e n e v a , Switzerland) and twenty years in university teaching and applied research (Universities of Toulouse a n d Bordeaux). His main scientific interests are automatic control, simulation, modelling and appfied mathematics. He has pubfished some ninety papers and five books. Elsevier Computers in Industry 14 (1990) 149-159 0166-3615/90/$3.50 © 1990 - Elsevier Science Publishers B.V.
1. Introduction As control engineers, we have been interested in Production Management Systems (PMS) since fifteen years. Our approach is to design Decision Support Systems (DSS) for PMS in the framework of Computer Integrated Manufacturing and Production Systems (CIMPS). Appropriate solutions for the CIMPS certainly require high-level intelligence and computing. The developments in AI science and techniques, in advanced computer programming languages, and in the so-called expert systems, however, give us stress, hope and fear. Many questions arise: What is this exactly? What and how to learn? Is this really useful? Can we do better? In this paper, we attempt to find some answers. The adopted line is: analyse the pertinence and the utility of AI techniques with respect to the design of PMS-DSS. We do this without humility and arrogance. We thus follow J. Hatvany whose main characteristics were: lucidity and humour. The analysis contains four steps, explained by the following story.
Story 1. "Abdula and PigMies" Abdula, tourist in Paris, is at the Place de la Concorde. He wants to go to Pigalles. He has several means to get there. He can walk, take a bus or underground fine, take a taxi or rent a car. He can hitch hike. All these means have a potentiality for reaching his goal. These means are pertinent. There exist many other means. For instance: write an integral differential equation; write a computer program; learn Swedish; develop a "traffic-jam" theory; measure the distance be-
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tween two street lamps. These means are not pertinent. Among the pertinent means, he has to choose one. He may make the following reasonings: - If I walk, I shall become tired before I ... ; - The bus? It is difficult to find the right one stopping near Pigalles; - The taxi-driver would take me for a grand tour; - Where are the offices to rent a car? Finally, he takes the metro line No. 1 connecting the station "Concorde" directly to the station "Place Pigalles". The utility of a pertinent means is some optimality which at the limits, is some feasibility. The four steps of our analysis are:
Step 1. Understanding of the Needs We have to understand the means we need to solve PMS problems, CIMPS problems and DSS problems. The task is not easy. Let us imagine that Abdula is with his wife Leila and his two sons S1 and $2. Leila wants "haute couture, jewels and shopping", S1 wants "opera and paintings", $2 wants "football match and rock and roll". For all this, the family has 50 francs and two days. The structure of a PMS is at least 10x times more complex with x ranging from 1 to 1000, than the extended Abdula-system.
Step 2. Understanding of the Pertinence of the A I Techniques Abdula is used to horse-and-donkey carriages. He can only recognise the pertinence of the modem means of transportation, if he has understood the general operating principles of these means.
Step 3. Understanding of the Utility of the A I Techniques Pertinence reflects potentiality. Utility depends on the effectiveness. In the Extended Abdula Problem, it is not evident to find quickly the right means for transporting the whole family. The solution might be "metro", " b u s + taxi", or " m e t r o + rent a car". The connection between "potentiality" and "utility" is direct if we have one activity with one single goal. It becomes complex if we have multi-goal, multi-constraint, multi-criteria, multi-activities.
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Step 4. Situation Algebra As results of the preceding understandings, we suggest a new modelling language which might be useful for the DSS design of PMS.
2. Elements and Problems in the Design of PMSDSS The needs for intelligence in the DSS-design depend on the problems arising in the PMS. The problems can only be formulated if we have some structural view of the PMS [22]. The PMS abstract modelling is above all an ontological problem. From the system-theoretical point of view [19], the choice of the elements and of the structuring links must be guided by four precepts: pertinence, teleology, globality and aggregation. In control fields, Forrester and Mesarovic have suggested since long time: Dynamical modelling, Hierarchical modelling, World modelling. All these models are lacking of teleology and then, pertinence. In Industrial Management Sciences, many attempts have been made to structure PMS [25]. They concern workshops, plant layouts, economical characterisation, computer networks, etc. All these attempts are lacking of globality. Since 1978, a number of PMS analysis methods have been developed [28]. Such as GRAI, MERISE, IDEF, SADT, SSAD, etc. The reference models contained in these methods are all lacking of globality. The key-point for an appropriate structural modelling is the teleology. This is illustrated by the following story.
Story 2. "'Cookner and New York City" John Cookner, 40 handsome, clever, adaptive, is a typical American self-made man (using three types of chromosones X, Y and Z). Once elected Governor of New York City, his ambition is to render every NewYorker happy. D u r i n g 5 years, he develops intensively: quality of imported goods; harbour equipments; large streets and roads; cultural, sportive and religious centers; high and low technologies; size of the Stock Exchar~e; free Medicare etc. The results are poor. New York City is just drugs, consumers, homosexuals, gangsters, coca-cola and hamburgers, kidnappings, trafficjams, etc. Even the rich people are unhappy. One night, during a dream, he receives two books:
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Cybernetics and Yi-King. The next days, after careful studies of the ethnics (East and West Europeans, Chinese- Japanese- Korean-Vietnamese, Indian-Arab, Porto Rican, Mexican) and their activities (economical, social, cultural, sportive, political), he finally establishes the Coo-coo Programme: (a) Define the common goal: New York City must produce one product called: nothing; (b) Plan carefully the activities of all the ethnics; (c) Self-supervise the activity results by Gigantic Boards placed at each street-corner; (d)Proclaim awards publicly each monday, with majorettes, orchestra, buffet and dance, to coocooners (whose activities lead to results closest to nothing). One year later, New York City is just joy, balance, harmony. The clever reader has certainly recognised in this story, the Generalised Kan-Ban Just-in-Time and Space Method. The basic philosophy is a triple stratification of (goal, activities and actors) in this cyclic and endless universe.
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PHYSICAL SYSTEM
mfl
Question
P,espor6e
(O) DECISION-SUPPORT SYSTEM
(R)
Fig. 1. Elements of the Production Management System.
2.1. Structuration of the PMS Goal of the PMS: Obtain the best coordination (in space) and the best synchronisation (in time) of all the activities of the physical system ( PS), so that the products are obtained with the best technical performances and economical efficiency (teleological aspect). 2.1.1. Macro Representation A macro representation of the system contains the following elements (Fig. 1): MS management system in charge of solving statically and dynamically the problems arising on PS; PS physical system in charge of production; DSS decision-support system in charge of helping the MS; E events, internal and external perturbations occuring to PS; I information from PS to MS; C commands from MS to PS; Q questions from MS to DSS; R responses from DSS to MS.
2.1.2. Elements of PS The physical system consists of the following elements: F1, F2, F3, F4 flows of materials, energy, finances, information and know-how; FN1 functional activities for the product definition (research and development, industrial set up, technical process planning, bill of materials); FN2 functional activities for the product manufacturing (machining, procurement, maintenance, quality control); SUP1 material support: machine-tools, apparatus, stock-yards, transportation equipments; SUP2 intellectual supports: methods, drawings, manuals, procedures; STRP structure of the product (variants, versions); STREQ structures of the equipment (machine-tools, cells, workshops, plants);
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STRT STRW
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structure of the processes (TPP, maintenance, control and test); structure of the executors.
2.1.3. Elements of MS FN3 functional activities of planning, deftning programmes of activities of all PS functional branches; FN4 functional activities of supervising; FN5 functional activities of dynamical adjustments; SUP3 intellectural supports for management: criteria, opportunity analysis; SUP4 material supports for management: possible actions consisting of modifying the flows F1 to F4, or the utilisation of SUP1 and SUP2; STRMG structure of the managers; STRPOL structure of the management policy (for example: the three-horizon policy (strategical, tactical and operational)); STRC structure of the maneuverability of the PS-capacity.
Implicit Knowledge
Language L (CME) ConceptualReferenceModels (CRM) Methodsand tools CME (MT)
Conceptual KnowledgeModel
Mathematical Modelling and Structuratlon OP (MME)
Language L (MME) MathematicalReference Models (MRM) [Methodsand tools MME (MT)
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Informational modelling and extraction OP (IME)
Language L (IME) ]Informational Reference /Mo~els (IRM) ~ - " ~ M e f h o d s and tools IME (MT)
3~ 2.1.4. Elements of DSS FN5 functional activities of elaborating the aids; SUP5 material support: computing elements; SUP6 intellectual support: algorithms, procedures; STRDATA data structure; STRKN knowledge structure; STRCD coordination programme structure; STRSlM simulation programme structure. 2.1.5. PMS Problem Statement Pbl local organisational problem (workshop scheduling, inventory control), one branch and one horizon; Pb2 2-level or 2-branch organisational problem (MRP); Pb3 several level or branch coordination and synchronising; Pb4 integrated PMS. 2.2. DSS Design Procedure
This procedure is viewed as a sequence of three activities: structuration, formalisation and com-
MathematicalKnowledgeModel
InformationalKnowledgeModel
Fig. 2. Decision Support System design procedure.
puterisation (Fig. 2). The elements of these activities are: KNO implicit knowledge contained in our mind, related to the PMS problems, elements and problem-solving procedures; OP (CME) activity of conceptual modelling and extraction; KN1 conceptual structured knowledge model; L (CME) language for CME; CME (MT) methods and tools (eventually computerised) for CME; CRM conceptual reference model (e.g., GRAI method and GRAI-tools); OP (MME) activity of mathematical (logical) modelling and extraction; L (MME) language for MME (e.g., first order predicate logic); MRM mathematical reference model (e.g., a formal, correct way of utilising the predicate logic); MME (MT) methods and tools for MME;
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KN2 OP (IME) L (IME) IRM IME (MT) KN3
formal logical structured knowledge model; activity of information modelling and extraction; language for IME (e.g., Prolog); information reference model (correct way for utilising Prolog); method and tools for IME; informational structured knowledge model.
2.2.1. Needs of Intelligence ND1 ontological representation of knowledge (cf. Websters': "ontology is a branch of metaphysics relating to the nature and relations of being or the kinds of existence"); ND2 solving of Pbl to Pb4; ND3 establishment of the three reference models CRM, M R M and IRM; ND4 semiotical establishment of languages, for each design step individually, and then globally if possible. 2.2.2. Remarks [22] (a) Operations Research type models are too restrictive. The hypothesis adopted in these models do not cope with the reality. (b) Control type models, such as hierarchical control, adaptive control, robust control are conceptually interesting, but they are only applicable to continuous activities. (c) PMS packages, such as scheduling, inventory control, MRP, can only be of local aid.
3. Potentiality Analysis of AI Techniques Until 1982, the literature on AI was limited. We had: (a) books from Winter, Nielsson, Hunt, etc.; (b) the series of proceedings of AI symposia from Edinburg; (c) AI Journal; (d) series of reports from MIT and SRI. Since 1982, a real explosion took place. Before making any potentiality analysis we encounter two problems: explore and classify. The following story is used for illustration. Story 3. "'Pontdu and the Amazonian Jungle" Mr. Pontdu, a well-known French ecologist, has achieved many non-governmental micro-realisations in developing countries. For example:
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an Ementhal cheese farm in Pakistan, a river-dam in Columbia, a village school in Bargundia, development of arable land in the desert of Southern Tanzania. Spring 1995, dry season, he comes to the Amazonian Jungle, Brazil. His intention is to find new resources for future mankind. He starts from Manhaus, goes south-west, towards Brazilia. Following Ria Unca, he penetrates the jungle. Very soon, it becomes difficult to go further. Intermingled lianas of child-arm size. Sky-rocketing trees with unperceivable tops. Crocodiles and big venomous snakes. Marshes with toxic gas. Clouds of mosquitos. Mr. Pontdu comes back to Europe and carefully prepares an exploration team. Ten CIA boys (Combattants d'Intelligence Artificielle, young Frenchmen specially trained in the city Anti-Atlantis). Astronaut cloth, anti-fire, anti-cold, anti-gas, anti-female. Laser-gun Type Lady Die (sorry, Lady Kill). Pocket computer, Light personel helicopter. Food pills. Autumn 1995, they find a sub-paradise with millions of deer. Going up west along the Alala chains, they find uranium, gold and diamond mines. On the other side of the Alala chains, Olala, kilometers of water falls, descending in a green lake three times the size of the Black Sea. Swans, lotus, salmons playing with sturgeons. Poor Mr. Pontdu now! What is, for the future mankind, the utility of all these snakes, crocodiles, deers, uranium and gold, smoked salmon and caviar? Our AI jungle is (partially) formed by the lots of announcements and brochures received September-December 1988. After filtering, they are roughly: (a) Symposia and Conferences: 73 items (really terrific!). Some topics at random: INCOM '89; Modelling the Innovation; Piocim 88; Ergonomie et IA 88; JIIA 88; Avignon 89--1es S E e t leurs applications; test et validation de logiciel; Expert Planning System; Convention unix 89; AI in Real-time Control; Computer Animation 89. (For each symposium, 2 days, 4,000 FF of registration and accomodation, totaling 146 days and 292,000 FF.) (b) New Journals: 11 items. Advanced Manufacturing Technology; AI and Manufacturing; CAD Eng. J.: Software Eng. J.; CIMS J. ; ES and Application; Modulad; Robotics; Future Generation Computer Systems. (We do not subscribe to any of these.)
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(c) Short Courses and Trainings: 13 items. Topics: Logistics and Packaging; High Performance Computing; Just-in Time Manufacturing and Kanban; Rrseaux de Communication; Ingrnieur Informatique. (Generally 3-5 days, 8000-10000 FF, good hotel, probably good meals.) (d) Brochures from software companies: 14 items. (e) Catalogues of publishers: 34 items. Around 60 books of interest. 60 × 300 = 18,000 FF). Obviously, there are three types of knowledge: (1) Useful knowledge (very few); (2) Useless knowledge (30%); (3) Harmful knowledge (65%). For the exploration, we use a laser gun to kill (2) and (3), we go up with helicopter to check the values of books, papers and articles. The AI treasures actually found are the following. (a) Categories of applications to game, image and signal processing, text processing. The intelligence potentialities are in knowledge representation and problem solving procedures. Little advancement, however, is found in the understanding of high-level abilities such as innovation, invention, creation, learning [17,20]. (b) Categories of applications to manufacturing mechanisms, the 3 generations of robots, adaptive numerical control, flexible manufacturing systems. The intelligence potentialities are in complex sensing and actuations. (c) Developments in data modelling and handling: relational database [11], HBDS (Hypergraph Based Data Structuring [4]), database management systems, multi-data-bases. The intelligence potentialities are in data representation in classes and in links between classes. However, there are two forms. From a "perception" point of view (extracting meaning from forms), there are two lacks. First, we need an organic and functional understanding of the database with respect to the problems. Second, we need formal and computable connections between the database and the mathematical logic. (d) Developments in formal languages for computer programming: structured programming, parallel processing, distributed programming, logical specification. The loose intelligence-potentiality is complicated reasoning. (e) Suggestions of knowledge modelling tools [30]: schemes, generalised objects, graphical tools
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(Petri-nets, Grai-nets, Grafcet). The intelligencepotentiality is to help man and machine to better perceive the meaning of the knowledge. (f) Developments in mathematical logics mostly in continuation of propositional logics and predicate logics [32,30,29]: logics by default, modal logics, temporal logics, non monotonous logics, conditional logics, rough applications, fuzzy logics, numerical quantifiers, causal models, state-modification logics, revision theory. The intelligence potentialities are in: capacity for describing complex elements and situations, capacity for formally representing reasoning processes. (g) Developments in Expert System shells [13] (KEE, Paradox, TRC, XI-Plus, XLisp, Guru, PC Prolog, Smalltalk 80, Knowledge Craft, MacProlog, Nexpert) and applications (medical, geology, finance, technological, diagnostic, CAD CAM, very few in PMS). The Expert System shells are "chocolate boxes", The ES applications are "chocolate". The chocolate boxes have little intelligence potentiality. The chocolate (the expertise) is not yet very significant to the understanding of the reasoning process.
4. Utility Analysis of AI Techniques Utility analysis is a judgement. A judgement is not necessarily "stick or carrot" (for the donkeys, not for the AI techniques). We need sublimation. Read the following story. Story 4. "'Salomon and Tree"
This sunny morning, Salomon, under his tree, treat the Lorry-Hardy case. Lorry said: " M y Lord, Hardy has killed my cow!" Hardy said: " M y lord, Lorry's cow has destroyed my crop". Salomon caresses his beard and looks at the top of the tree. High, high! He then pronounces steadily "will give you ten cows. You breed them in common. In two years, you will get back your cow and your crop." For two years, Lorry and Hardy continue to work hard. Ten cows become a hundred, and then a thousand cows. They make cream from the abundant milk. They use on part of the cream for face throwing in their movie pictures. The other part is iced, because the country is cold. This is how ice-cream came about.
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Sublimation is to lift up. For our utility analysis, we replace our "ten cows" by the following intelligence requirements, derived from ND1 to ND4 (end of Section 2). R Q 1 - - T h e vocabulary of the modelling languages must be understandable by all the partners involved in the DSS design (managers, home engineers, users, analysts, experts, computing specialists). Reason: facilitate communication, problem-formulation, extraction of knowledge and expertise. RQ2--Transformafion laws, features and behaviours must be formalised so that they are computable. Reason: facilitate the study of observability (diagnostic) and of attainability (therapy), basis of all decision making. R Q 3 - - T h e semantics and the syntax of the modelling language must be able to describe all the features of the environment. Reason: facilitate the inclusion of contextual conditions of the problems and decision makings. R Q 4 - - T h e modelling languages must be semiotical, i.e. the symbols of the semantics and syntax must favor the pragmatics [21]. Reasons: facilitate inspirations during reasoning. The utility of various AI techniques can be analysed as follows. 4.1. Utility 1. Around the Concept "'Object"
(a) Codd [8] 'introduced the definition of data: DATA: (Object, Attribute, Value). The concept "object" in principle covers here a simple element; the generalised concept "attribute", including linguistical characterisation permits the establishment of "Relational Data Base". The structural features can be formalised by binary algebra and predicate logics. (b) Bouill6 [4] introduced the definition: DATA: (Class, Objects, Attribute, Links) in the set theoretical sense. The concept "object" is generalised to include any "entity". Such a definition permits the establishment of hierarchical structures (hyperclass, class, sub-class) and parallel structures (purpose links). The structural features can be formalised in the framework of Hypergraph theory. (c) In the object oriented language [9,31], the concept "object" is generalised to include "ele-
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ment", "entity", "transformation law", "procedure". This permits to establish semantic and syntactic relations between various levels of knowledge and know-how. Various advanced logics attempt to formalise the structural features of knowledge. Only partial results have been obtained [29]. All the expressions used are easily understandable, satisfying RQ1. The computability is only obtained in simple cases. RQ2 is only partially satisfied. 4.2. Utility 2. Around "'Abstract Types"
In programming languages (C, PL1, Fortran, Pascal), the definition of an abstract type is: AT: (Values, Operators), e.g., integers with arithmetic operators, binary numbers with logical binary operations. Many extensions have been suggested, e.g. (a) Image processing [2]. Values: (elementary geometrical figures); Operators: (construction laws). (b) Icon algebra [6]. Values: (icons); Operators: (manipulators). Generalisation: to design an abstract type is to construct a modelling language, of which the triple (semantics, syntax, pragmatics) satisfies the semiotical property, but only in a specific universe. In that specific universe, all the requirements RQ1 to RQ4 are satisfied. 4.3. Utility 3. "'Predicate Logic and Others"
(a) First order predicate logic. The used formal elements: variables and constants, functions, predicates, terms, clauses are general and understandable. The limits of the generality lie in the defined quantifiers and connectors. The suggested inference laws (modus ponens, modus tolens, if then else) and inference engines (chainings) are in lack of transcendance [7]. Reason 1: the implication connector suffers from many paradoxes. Reason 2: the variety of the environments are not sufficiently taken into account. This modelling language satisfies partially RQ1, RQ2 and RQ4. It does not satisfy RQ3. (b) Other advanced logics [30,29]. General attempts take into account imprecision, incompleteness, fuzziness, relevance conditions, defaults, unclear causes, etc. These modelling techniques at-
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tempt to satisfy RQ3. The used formalisms tend to become more and more obscure. They satisfy neither RQ1, nor RQ4.
4.4. Utility 4. Knowledge Classification Various knowledge classifications have been established. We adopt the following one [18,23,24]: Level 1--Element E: simplest entity, physical or abstract. Level 2--Complex element CE: formed with several E. The connectives may be arithmetic, algebraic, logical, linguistical, vectorial (orthogonal or not). Level 3--Transformation T: Operating law transforming one E (or CE) into another E (or CE) Examples: application, transfer function, morphism in category theory, cause to effect relation. Level 4--Statement S: formed with several T to ascertain a truth. Examples: axiom, theorem, predicate clauses, rules in Expert Systems. Level 5--Procedure PR: formed with several S to solve one problem. Examples: algorithm, metarules in Expert Systems. Level 6--Programme PG: formed with several PR to solve several correlated problems. Example: software in Expert Systems. The used expressions to denote the knowledge are generic. They bear specific names in each modelling language. The 6 levels are intrinsically related in a hierarchical way. They may be represented by a semantic tree. The utility in the PMSDSS design (Fig. 3) is to form a Knowledge Reference Model guiding Knowledge Extraction from
the physical system (PS) and management system (MS) and knowledge programming in the computerised DSS. The characteristic features in practical use are their states or situations (next section). Each piece of knowledge (or state or situation) can be viewed as a generalised object in Godd's sense: knowledge: (state or situation, attribute value).
5. Situation Algebra After analysing the intelligence needs of our PMS problems, and the intelligence lacks of the existing AI techniques, we attempt to construct a satisfactory modelling language.
Story 5. "Yuko and the Hole in the Sky" Billions of years ago, someone decides to separate the sky and the earth. After the big explosion, a hole remains in the sky. Yuko, a very beautiful lady, picks a large inflammed rock, and puts it in the hole. Afterwards, the harmony is established in our universe. The harmony is the expected satisfactory modelling language for the PMS°DSS design. The hole is the AI lacks. The rock is the activity-situation algebra [26]. Basic problem-solving principle: Establish strategies and tactics for qualitative problem-solving first, guide the search of decision-making conditions towards quantitative problem-solving.
5.1. Vocabulary
Definition. SUPM, if)
I PhysicalSystem(PS) ~ ~ r Management System ( M S ) ~ Ex~rElct[on
) Decision-SupportSystem
T
Programming Langages
Fig. 3.
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ACTIVITY:
A = (f,
FN,
SUPI,
This is a 5-tuple machine with flows f, functionality behaviour FN, intellectual support SUPI, material ressource support SUPM, final flow ft. This definition covers one activity, one programme of activities. This system concept permits representation in an integrated way the manifestations of all the sub-systems (PS, MS, DSS) included in the PMS.
Definition. SITUATION: SIT = general symbol; SIT(a), SIT(element of A), SIT(KN). This is a generalisation of the concept of state. It contains however a prospective significance.
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The concept state means "what it is". The concept situation means "what it is and what must be done in the future". Examples: bad economic situation of a country, insufficient machine preparation. The characterisation power lies in the appropriate attributes. - Situation of a flow SIT(f), of a support SIT(SUPI) or SIT(SUPM), the attribute is a STATE (a speed of an amount, a type of machine), the values can be measured on various scales: real numbers, binary or multivalent numbers, linguistic expressions. It can be a scalar or a vector. - Situation of an activity SIT(A), the attribute is the potentiality: POT(f, FN, SUPI, SUPM). The values are the potentialities of the component-tuples. The potentiality is to be defined relatively with respect to the activity goal (PS-situation with respect to the production goal, MS-activities with respect to the organisational goal, DSSsituation with respect to the aid-goal).
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(b) Knowledge-appropriation manipulation: APKN. Aim: when knowledge is not appropriate for PS, MS or DSS-activities, find a way to reach appropriate knowledge. This concerns both f, FN, SUPI, SUPM, ff. The operation comprises in principle two steps. Step 1: discover the non-appropriateness in the knowledge. Step 2: collect information to suppress the non-appropriateness. The general formula is: APKN(ATTRIBUTE) : SIT(KN (--,ATTRIBUTE)) REASON(ATTRIBUTE) (KN(ATTRIBUTE)) The various useful attributes are: consistency, completness, necessity, sufficiency non fuzzyness, correct formulation, - correct formalisation.
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-
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5.2. Elements of the Algebra
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Manipulators: MAP : SIT 1 -* SIT 2 Generalisation of the "'operator" concept. A MAP manipulates situations. (a) Evaluation manipulators (determined to evaluate the results of activities, states of support, functionalities, flows, the importance of events or perturbations, the appropriateness of decisionmaking elements, the qualities of DSS elements). Result evaluation: EVR EVR: (actual ff, desired fir) --* SIT(if) Support evaluation: EVSUP EVSUP: (actual SUP, Programmed SUP)--* SIT(SUP) Element debugger: DEBEL DEBEL: SIT(if) ~ SIT(A) (Find the A-element which influences SIT(if)) Horizon debugger: DEBH DEBH: SIT(if) --, SIT(SUP3, SUP4) (find the right horizon (ST, MT, or LT), the decisional-framework which corresponds to SIT(if)) Branch debugger. DEBBR -
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(c) Actuation manipulators. Aim: modify the situations SIT(A) or SIT(element-A) of PS, MS, DSS-activities for the dynamic adjustments. - DFLOW: SIT(f)1 ~ SIT(f)2 (where f is the flow state from f0 to ff of an activity and fl, f2. . . . . ff is a programme of activities) For PS; {F1, F2, F3}, Fn For MS: I, C For DSS: Q, R - MODSUP: SIT(SUP)I -~ SIT(SUP)2 For PS: SUP1, SUP2 (methods and tools) For MS; SUP3, SUP4: (strategies, tactics), field of actions, frameworks For DSS: SUP5, SUP6: data-structure, rulestructure, procedures, hardwares.
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DEBBR: SIT(ff/BI) --, SIT(A/B J) (find the activity A of some branch BJ which influences SIT(if) of branch BI).
5.3. Application Example Problem: find the reason of a bottleneck occuring in front of a workstation (WS). Procedure for the solution:
Step 1. Determine the related activities: A2: programmed activity on WS, Al: programmed activity preceeding WS;
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Step 2. Evaluate SIT(ff/A1);
if Actual(ff/A1) larger than the programmed value, evaluate ( S I T ( S U P / A 1 ) ; if Actual(ff/A1) smaller or equal, evaluate SIT(SUP/A2); Step 3. If S I T ( S U P / A 2 ) smaller than the programmed value, make A P K N ( S I T ( A 3 / MT) to find complete knowledge on medium-horizon planning; Determine S I T ( A 3 / M T ) ; Step 4. Evaluate decisions on S U P / A 3 / M T (possible actions, i.e.: decision made on W S / A 2 capacities); If the planned values are too small, make (MODSUP(WS): S I T ( S U P / A 2 ) a c t u a l SIT(SUP/A2)better. The situational algebra underlies the following problem solving principle: establish strategies and tactics for qualitative problem solving firstly, guide the search of decision making conditions for quantitative problem solving. This principle is derived from the following considerations. A production management is perfect, if, for all branches of activities and for all horizons (long, medium, short), we succeed to make: (a) feasible planning, where goals and activities are p r o g r a m m e d in executable details, where intellectual and material supports are predetermined coherently; (b) efficient supervising, where dispatchings, results, supports, interrelations are well coordinated and well synchronised; (c) appropriate dynamic adjustings, where unpredicted perturbations can be debugged and where support margins and time margins are sufficiently large so that correcting actions are possible. (This situation is reached in kanban applications, because in line-type manufacturing where conditions are well defined, there is consensus a m o n g the human executors to achieve coordination). In practice, this ideal situation is rarely reached, because: (a) Internal and external capacities are not well known in advance, so the planning is not always feasible. (b) communication between executors suffers f r o m not being precise, individualism and ignorance, so global supervision is not always efficient.
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(c) few margins are installed preventively in the flow control and the support utilisation (because this is expensive), so the dynamic adjustings are not appropriate.
6. Conclusion Story 6. "The Cat and the Fish"
One day, the young David attaches a b a m b o o stick on the back of the cat Sultan. At the high extremity of the stick, a nice little fish is fixed. Sultan spreads his claws and tries to catch the fish above his head, but cannot reach it. Sultan j u m p s higher. H e climbs the steps, reaches the terrasse on the roof. A big j u m p and he falls on the ground. He reaches again the steps, he c l i m b s . . . Five thousand year ago, Wu-Shi has already explained in Yi-King, the nature of this Y i n g - Y a n g cycle. In this paper, we have only made one jump.
References [1] A. Barr and E.A. Feigenbaum, Le Manuel de L'IA, tome 1, Eyrolles, Paris, 1986. [2] A. Belaid, "Un langage de traitement d'images fond6 sur des concepts de types abstraits", Tech. Sci. lnf. (Paris), Vol. 4, No. 3, 1985, pp. 309-343. [3] G. Benchimol, P. Levine and J.Ch. Pomeroi, Systimes Experts darts I'Entreprise, Hermes (1986) 219 p. [4] F. Bouill6, Un module universel de banque de donn~es. Thrse docteur d'rtat, Universit6 Paris 4, 1977. [5] C.C. Chang and H.J. Keisler, Model Theory, North Holland, Amsterdam, 1973, 554 pp. [6] S.K. Chang, "Icons semantics--a formal approach to icon system design", lnt. J. Pattern Reognition A1, Vol. 1, No. 1, April 1987. [7] H. Chouraqui, M. Prade and S. Dubois, "Modelisation du raisonnement et de la connaissance", Tech. Sci. Inf. (Paris), Vol. 4, No. 4, 1985, pp 391-399. [8] E.F. Codd, "A relational model of data for large shared databanks", CACM, Vol. 6, June 1970. [9] D. Colnel, G. Masini and A. Napoli, Les langages orient$s objets, Rapport CRIN 86-R-077, Centre de Recherche en Informatique de Nancy, 1986. [10] C.J. Date, An introduction to Datchas System, 3rd edn., Addison-Wesley, Reading, MA, 1982. [11] C. Delobel and M. Adiba, Base de Donndes et Systdmes Relationnels, Dunod, Paris, 1982. [12] G. Doumeingts, B. Valespir, D. Darricau and M. Roboam, "Design methodology for advanced manufacturing systems", Computers in Industry, Vol. 9, 1987, pp. 271"296. [13] E. Erolanta, H. Hammainen, J. Alasucanto, L. Matins and G. Doumeingts, "An evaluation of expert systems design tools for production manageraent", private report, 1988.
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[14] J.F. Gallouin, Transfer des Connaissances: System,s Experts-Techniques et M$thodes, Eyrolles, Paris, 1988, 168 PP.
[15] A. Hart, Acquisition du savoir pour les syst~mes experts, Mason, Paris, 1988. [16] Hayes-Roth, Building Expert Systems, Addison-Wesley, Reading, MA, 1983, 440 pp. [17] JIL. Lauri6re, Intelligence Artificielle, tome 1: R$solution de probl~mes par l'home et par la machine, Eyrolles, Paris, 1986, 473 pp. [18] J.L. Lauri6re, Intelligence Artificielle, tome 2: Representation des connaissances, Eyrolles, 1988, 268 pp. [19] J.L. Lemoigne, Les Syst~mes de D#cision dans les Organisations, Presses universitaires fran~aises, Paris, 1974. [20] I. McCarthy, "Epistemological problems in AI", Proc. IJAJ, MIT, Cambridge, MA, 1977, pp. 1038-1044. [21] D.A. Pospelov, "Semiotic models in control systems", in: Cybernetics Today, MIR Publ., Moscow, 1984. [22] L. PUn, "Static and dynamical production plannings", in: Proc. 4th IFAC Symposium on Large Scale Systems, August 26-29, 1986, Zurich. [23] L. Pun, Syst~mes Industriels d'Intelligence Artificielle, Editest, Paris, 1984; Chinese version, Friendship Publ. Co, Beijing, 1987; English version, Plenum, London, 1990. [24] L. Pun, "Computer-aided management in developing countries", in: Proc. IFAC World Congress X, August 1987, Munich.
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[25] L. Pun, "Methodology for structuring distributed intelligence in computer-aided production management", in: Proc. IFAC Symposium on Distributed Intelligence, July 1-6, 1988, Varna. [26] L. Pun, "Activity and situation modelling for production management systems", in: Proc. Int. Conf. on System Science and Engineering ICSSE'88, July 25-28, 1988, Beijing. [27] M. Roboam and L. Pun, "Utility of design methodology for advanced manufacturing systems", Comput. Integr. Manuf. Syst., February 1989. [28] M. Roboam, M. Zanettin and L. Pun, " G R A I - I D E F O MERISE(GIM): integrated methodology to analyse and design manufacturing systems", Comput. lntegr. Manuf. Syst., May 1989. [29] L. Somb6, "Inf6rences non classiques et IA--Ebauche de comparaison sur un exemple", Actes des journ~es nationales du PRC 1,4, Toulouse, 1988, 94 pp. [30] A. Thayse et al., Approche Logique de l'Intelligence Artificielle, tome 1: de la logique classique ~ la programmation logique, Dunod, Paris, 1988, 389 pp. [31] D. Tsitchritsis, ed., Objects and Things, Centre Universitaire d'Informatique de Gen6ve, 1987. [32] R. Turner, Logiques pour l'lA, Masson, Paris, 1985, 121 pP.
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Pertinence and Utility of Artificial Intelligence Techniques for Production Management Systems L. Pun Universit~ de Bordeaux L Laboratoire de Productique et d'Automatique GRAI, 351 cours de la Liberation, 33405 Talence, France Imitation of lucidity and humour were two main characteristics of J. Hatvany. The paper contains five parts. (1) Definitions of pertinence and utility. (2) Problematics of production management systems to determine intelligence need. (3) Potentiality analysis of AI techniques. (4) Utility analysis of AI techniques. (5) Suggestions of a filling rock: a situation algebra.
Keywords: AI techniques, Production management, Decision support systems, Situation algebra.
Pun received his B.E.E. and B. M a t h from Aurora University, Shanghai, and his Dipl. Ing.-ESE from Ecole Sup~trieure d'Electricit~, Paris in 1951. He earned the degree of Ing.-Dr, from the University of Grenoble in 1954 and State Dr. of Sciences from the University of Toulouse. He has worked some twenty years in Industrial D e v e l o p m e n t ( G e n e v a , Switzerland) and twenty years in university teaching and applied research (Universities of Toulouse a n d Bordeaux). His main scientific interests are automatic control, simulation, modelling and appfied mathematics. He has pubfished some ninety papers and five books. Elsevier Computers in Industry 14 (1990) 149-159 0166-3615/90/$3.50 © 1990 - Elsevier Science Publishers B.V.
1. Introduction As control engineers, we have been interested in Production Management Systems (PMS) since fifteen years. Our approach is to design Decision Support Systems (DSS) for PMS in the framework of Computer Integrated Manufacturing and Production Systems (CIMPS). Appropriate solutions for the CIMPS certainly require high-level intelligence and computing. The developments in AI science and techniques, in advanced computer programming languages, and in the so-called expert systems, however, give us stress, hope and fear. Many questions arise: What is this exactly? What and how to learn? Is this really useful? Can we do better? In this paper, we attempt to find some answers. The adopted line is: analyse the pertinence and the utility of AI techniques with respect to the design of PMS-DSS. We do this without humility and arrogance. We thus follow J. Hatvany whose main characteristics were: lucidity and humour. The analysis contains four steps, explained by the following story.
Story 1. "Abdula and PigMies" Abdula, tourist in Paris, is at the Place de la Concorde. He wants to go to Pigalles. He has several means to get there. He can walk, take a bus or underground fine, take a taxi or rent a car. He can hitch hike. All these means have a potentiality for reaching his goal. These means are pertinent. There exist many other means. For instance: write an integral differential equation; write a computer program; learn Swedish; develop a "traffic-jam" theory; measure the distance be-
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tween two street lamps. These means are not pertinent. Among the pertinent means, he has to choose one. He may make the following reasonings: - If I walk, I shall become tired before I ... ; - The bus? It is difficult to find the right one stopping near Pigalles; - The taxi-driver would take me for a grand tour; - Where are the offices to rent a car? Finally, he takes the metro line No. 1 connecting the station "Concorde" directly to the station "Place Pigalles". The utility of a pertinent means is some optimality which at the limits, is some feasibility. The four steps of our analysis are:
Step 1. Understanding of the Needs We have to understand the means we need to solve PMS problems, CIMPS problems and DSS problems. The task is not easy. Let us imagine that Abdula is with his wife Leila and his two sons S1 and $2. Leila wants "haute couture, jewels and shopping", S1 wants "opera and paintings", $2 wants "football match and rock and roll". For all this, the family has 50 francs and two days. The structure of a PMS is at least 10x times more complex with x ranging from 1 to 1000, than the extended Abdula-system.
Step 2. Understanding of the Pertinence of the A I Techniques Abdula is used to horse-and-donkey carriages. He can only recognise the pertinence of the modem means of transportation, if he has understood the general operating principles of these means.
Step 3. Understanding of the Utility of the A I Techniques Pertinence reflects potentiality. Utility depends on the effectiveness. In the Extended Abdula Problem, it is not evident to find quickly the right means for transporting the whole family. The solution might be "metro", " b u s + taxi", or " m e t r o + rent a car". The connection between "potentiality" and "utility" is direct if we have one activity with one single goal. It becomes complex if we have multi-goal, multi-constraint, multi-criteria, multi-activities.
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Step 4. Situation Algebra As results of the preceding understandings, we suggest a new modelling language which might be useful for the DSS design of PMS.
2. Elements and Problems in the Design of PMSDSS The needs for intelligence in the DSS-design depend on the problems arising in the PMS. The problems can only be formulated if we have some structural view of the PMS [22]. The PMS abstract modelling is above all an ontological problem. From the system-theoretical point of view [19], the choice of the elements and of the structuring links must be guided by four precepts: pertinence, teleology, globality and aggregation. In control fields, Forrester and Mesarovic have suggested since long time: Dynamical modelling, Hierarchical modelling, World modelling. All these models are lacking of teleology and then, pertinence. In Industrial Management Sciences, many attempts have been made to structure PMS [25]. They concern workshops, plant layouts, economical characterisation, computer networks, etc. All these attempts are lacking of globality. Since 1978, a number of PMS analysis methods have been developed [28]. Such as GRAI, MERISE, IDEF, SADT, SSAD, etc. The reference models contained in these methods are all lacking of globality. The key-point for an appropriate structural modelling is the teleology. This is illustrated by the following story.
Story 2. "'Cookner and New York City" John Cookner, 40 handsome, clever, adaptive, is a typical American self-made man (using three types of chromosones X, Y and Z). Once elected Governor of New York City, his ambition is to render every NewYorker happy. D u r i n g 5 years, he develops intensively: quality of imported goods; harbour equipments; large streets and roads; cultural, sportive and religious centers; high and low technologies; size of the Stock Exchar~e; free Medicare etc. The results are poor. New York City is just drugs, consumers, homosexuals, gangsters, coca-cola and hamburgers, kidnappings, trafficjams, etc. Even the rich people are unhappy. One night, during a dream, he receives two books:
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Cybernetics and Yi-King. The next days, after careful studies of the ethnics (East and West Europeans, Chinese- Japanese- Korean-Vietnamese, Indian-Arab, Porto Rican, Mexican) and their activities (economical, social, cultural, sportive, political), he finally establishes the Coo-coo Programme: (a) Define the common goal: New York City must produce one product called: nothing; (b) Plan carefully the activities of all the ethnics; (c) Self-supervise the activity results by Gigantic Boards placed at each street-corner; (d)Proclaim awards publicly each monday, with majorettes, orchestra, buffet and dance, to coocooners (whose activities lead to results closest to nothing). One year later, New York City is just joy, balance, harmony. The clever reader has certainly recognised in this story, the Generalised Kan-Ban Just-in-Time and Space Method. The basic philosophy is a triple stratification of (goal, activities and actors) in this cyclic and endless universe.
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PHYSICAL SYSTEM
mfl
Question
P,espor6e
(O) DECISION-SUPPORT SYSTEM
(R)
Fig. 1. Elements of the Production Management System.
2.1. Structuration of the PMS Goal of the PMS: Obtain the best coordination (in space) and the best synchronisation (in time) of all the activities of the physical system ( PS), so that the products are obtained with the best technical performances and economical efficiency (teleological aspect). 2.1.1. Macro Representation A macro representation of the system contains the following elements (Fig. 1): MS management system in charge of solving statically and dynamically the problems arising on PS; PS physical system in charge of production; DSS decision-support system in charge of helping the MS; E events, internal and external perturbations occuring to PS; I information from PS to MS; C commands from MS to PS; Q questions from MS to DSS; R responses from DSS to MS.
2.1.2. Elements of PS The physical system consists of the following elements: F1, F2, F3, F4 flows of materials, energy, finances, information and know-how; FN1 functional activities for the product definition (research and development, industrial set up, technical process planning, bill of materials); FN2 functional activities for the product manufacturing (machining, procurement, maintenance, quality control); SUP1 material support: machine-tools, apparatus, stock-yards, transportation equipments; SUP2 intellectual supports: methods, drawings, manuals, procedures; STRP structure of the product (variants, versions); STREQ structures of the equipment (machine-tools, cells, workshops, plants);
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STRT STRW
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structure of the processes (TPP, maintenance, control and test); structure of the executors.
2.1.3. Elements of MS FN3 functional activities of planning, deftning programmes of activities of all PS functional branches; FN4 functional activities of supervising; FN5 functional activities of dynamical adjustments; SUP3 intellectural supports for management: criteria, opportunity analysis; SUP4 material supports for management: possible actions consisting of modifying the flows F1 to F4, or the utilisation of SUP1 and SUP2; STRMG structure of the managers; STRPOL structure of the management policy (for example: the three-horizon policy (strategical, tactical and operational)); STRC structure of the maneuverability of the PS-capacity.
Implicit Knowledge
Language L (CME) ConceptualReferenceModels (CRM) Methodsand tools CME (MT)
Conceptual KnowledgeModel
Mathematical Modelling and Structuratlon OP (MME)
Language L (MME) MathematicalReference Models (MRM) [Methodsand tools MME (MT)
-)~
Informational modelling and extraction OP (IME)
Language L (IME) ]Informational Reference /Mo~els (IRM) ~ - " ~ M e f h o d s and tools IME (MT)
3~ 2.1.4. Elements of DSS FN5 functional activities of elaborating the aids; SUP5 material support: computing elements; SUP6 intellectual support: algorithms, procedures; STRDATA data structure; STRKN knowledge structure; STRCD coordination programme structure; STRSlM simulation programme structure. 2.1.5. PMS Problem Statement Pbl local organisational problem (workshop scheduling, inventory control), one branch and one horizon; Pb2 2-level or 2-branch organisational problem (MRP); Pb3 several level or branch coordination and synchronising; Pb4 integrated PMS. 2.2. DSS Design Procedure
This procedure is viewed as a sequence of three activities: structuration, formalisation and com-
MathematicalKnowledgeModel
InformationalKnowledgeModel
Fig. 2. Decision Support System design procedure.
puterisation (Fig. 2). The elements of these activities are: KNO implicit knowledge contained in our mind, related to the PMS problems, elements and problem-solving procedures; OP (CME) activity of conceptual modelling and extraction; KN1 conceptual structured knowledge model; L (CME) language for CME; CME (MT) methods and tools (eventually computerised) for CME; CRM conceptual reference model (e.g., GRAI method and GRAI-tools); OP (MME) activity of mathematical (logical) modelling and extraction; L (MME) language for MME (e.g., first order predicate logic); MRM mathematical reference model (e.g., a formal, correct way of utilising the predicate logic); MME (MT) methods and tools for MME;
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KN2 OP (IME) L (IME) IRM IME (MT) KN3
formal logical structured knowledge model; activity of information modelling and extraction; language for IME (e.g., Prolog); information reference model (correct way for utilising Prolog); method and tools for IME; informational structured knowledge model.
2.2.1. Needs of Intelligence ND1 ontological representation of knowledge (cf. Websters': "ontology is a branch of metaphysics relating to the nature and relations of being or the kinds of existence"); ND2 solving of Pbl to Pb4; ND3 establishment of the three reference models CRM, M R M and IRM; ND4 semiotical establishment of languages, for each design step individually, and then globally if possible. 2.2.2. Remarks [22] (a) Operations Research type models are too restrictive. The hypothesis adopted in these models do not cope with the reality. (b) Control type models, such as hierarchical control, adaptive control, robust control are conceptually interesting, but they are only applicable to continuous activities. (c) PMS packages, such as scheduling, inventory control, MRP, can only be of local aid.
3. Potentiality Analysis of AI Techniques Until 1982, the literature on AI was limited. We had: (a) books from Winter, Nielsson, Hunt, etc.; (b) the series of proceedings of AI symposia from Edinburg; (c) AI Journal; (d) series of reports from MIT and SRI. Since 1982, a real explosion took place. Before making any potentiality analysis we encounter two problems: explore and classify. The following story is used for illustration. Story 3. "'Pontdu and the Amazonian Jungle" Mr. Pontdu, a well-known French ecologist, has achieved many non-governmental micro-realisations in developing countries. For example:
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an Ementhal cheese farm in Pakistan, a river-dam in Columbia, a village school in Bargundia, development of arable land in the desert of Southern Tanzania. Spring 1995, dry season, he comes to the Amazonian Jungle, Brazil. His intention is to find new resources for future mankind. He starts from Manhaus, goes south-west, towards Brazilia. Following Ria Unca, he penetrates the jungle. Very soon, it becomes difficult to go further. Intermingled lianas of child-arm size. Sky-rocketing trees with unperceivable tops. Crocodiles and big venomous snakes. Marshes with toxic gas. Clouds of mosquitos. Mr. Pontdu comes back to Europe and carefully prepares an exploration team. Ten CIA boys (Combattants d'Intelligence Artificielle, young Frenchmen specially trained in the city Anti-Atlantis). Astronaut cloth, anti-fire, anti-cold, anti-gas, anti-female. Laser-gun Type Lady Die (sorry, Lady Kill). Pocket computer, Light personel helicopter. Food pills. Autumn 1995, they find a sub-paradise with millions of deer. Going up west along the Alala chains, they find uranium, gold and diamond mines. On the other side of the Alala chains, Olala, kilometers of water falls, descending in a green lake three times the size of the Black Sea. Swans, lotus, salmons playing with sturgeons. Poor Mr. Pontdu now! What is, for the future mankind, the utility of all these snakes, crocodiles, deers, uranium and gold, smoked salmon and caviar? Our AI jungle is (partially) formed by the lots of announcements and brochures received September-December 1988. After filtering, they are roughly: (a) Symposia and Conferences: 73 items (really terrific!). Some topics at random: INCOM '89; Modelling the Innovation; Piocim 88; Ergonomie et IA 88; JIIA 88; Avignon 89--1es S E e t leurs applications; test et validation de logiciel; Expert Planning System; Convention unix 89; AI in Real-time Control; Computer Animation 89. (For each symposium, 2 days, 4,000 FF of registration and accomodation, totaling 146 days and 292,000 FF.) (b) New Journals: 11 items. Advanced Manufacturing Technology; AI and Manufacturing; CAD Eng. J.: Software Eng. J.; CIMS J. ; ES and Application; Modulad; Robotics; Future Generation Computer Systems. (We do not subscribe to any of these.)
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(c) Short Courses and Trainings: 13 items. Topics: Logistics and Packaging; High Performance Computing; Just-in Time Manufacturing and Kanban; Rrseaux de Communication; Ingrnieur Informatique. (Generally 3-5 days, 8000-10000 FF, good hotel, probably good meals.) (d) Brochures from software companies: 14 items. (e) Catalogues of publishers: 34 items. Around 60 books of interest. 60 × 300 = 18,000 FF). Obviously, there are three types of knowledge: (1) Useful knowledge (very few); (2) Useless knowledge (30%); (3) Harmful knowledge (65%). For the exploration, we use a laser gun to kill (2) and (3), we go up with helicopter to check the values of books, papers and articles. The AI treasures actually found are the following. (a) Categories of applications to game, image and signal processing, text processing. The intelligence potentialities are in knowledge representation and problem solving procedures. Little advancement, however, is found in the understanding of high-level abilities such as innovation, invention, creation, learning [17,20]. (b) Categories of applications to manufacturing mechanisms, the 3 generations of robots, adaptive numerical control, flexible manufacturing systems. The intelligence potentialities are in complex sensing and actuations. (c) Developments in data modelling and handling: relational database [11], HBDS (Hypergraph Based Data Structuring [4]), database management systems, multi-data-bases. The intelligence potentialities are in data representation in classes and in links between classes. However, there are two forms. From a "perception" point of view (extracting meaning from forms), there are two lacks. First, we need an organic and functional understanding of the database with respect to the problems. Second, we need formal and computable connections between the database and the mathematical logic. (d) Developments in formal languages for computer programming: structured programming, parallel processing, distributed programming, logical specification. The loose intelligence-potentiality is complicated reasoning. (e) Suggestions of knowledge modelling tools [30]: schemes, generalised objects, graphical tools
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(Petri-nets, Grai-nets, Grafcet). The intelligencepotentiality is to help man and machine to better perceive the meaning of the knowledge. (f) Developments in mathematical logics mostly in continuation of propositional logics and predicate logics [32,30,29]: logics by default, modal logics, temporal logics, non monotonous logics, conditional logics, rough applications, fuzzy logics, numerical quantifiers, causal models, state-modification logics, revision theory. The intelligence potentialities are in: capacity for describing complex elements and situations, capacity for formally representing reasoning processes. (g) Developments in Expert System shells [13] (KEE, Paradox, TRC, XI-Plus, XLisp, Guru, PC Prolog, Smalltalk 80, Knowledge Craft, MacProlog, Nexpert) and applications (medical, geology, finance, technological, diagnostic, CAD CAM, very few in PMS). The Expert System shells are "chocolate boxes", The ES applications are "chocolate". The chocolate boxes have little intelligence potentiality. The chocolate (the expertise) is not yet very significant to the understanding of the reasoning process.
4. Utility Analysis of AI Techniques Utility analysis is a judgement. A judgement is not necessarily "stick or carrot" (for the donkeys, not for the AI techniques). We need sublimation. Read the following story. Story 4. "'Salomon and Tree"
This sunny morning, Salomon, under his tree, treat the Lorry-Hardy case. Lorry said: " M y Lord, Hardy has killed my cow!" Hardy said: " M y lord, Lorry's cow has destroyed my crop". Salomon caresses his beard and looks at the top of the tree. High, high! He then pronounces steadily "will give you ten cows. You breed them in common. In two years, you will get back your cow and your crop." For two years, Lorry and Hardy continue to work hard. Ten cows become a hundred, and then a thousand cows. They make cream from the abundant milk. They use on part of the cream for face throwing in their movie pictures. The other part is iced, because the country is cold. This is how ice-cream came about.
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Sublimation is to lift up. For our utility analysis, we replace our "ten cows" by the following intelligence requirements, derived from ND1 to ND4 (end of Section 2). R Q 1 - - T h e vocabulary of the modelling languages must be understandable by all the partners involved in the DSS design (managers, home engineers, users, analysts, experts, computing specialists). Reason: facilitate communication, problem-formulation, extraction of knowledge and expertise. RQ2--Transformafion laws, features and behaviours must be formalised so that they are computable. Reason: facilitate the study of observability (diagnostic) and of attainability (therapy), basis of all decision making. R Q 3 - - T h e semantics and the syntax of the modelling language must be able to describe all the features of the environment. Reason: facilitate the inclusion of contextual conditions of the problems and decision makings. R Q 4 - - T h e modelling languages must be semiotical, i.e. the symbols of the semantics and syntax must favor the pragmatics [21]. Reasons: facilitate inspirations during reasoning. The utility of various AI techniques can be analysed as follows. 4.1. Utility 1. Around the Concept "'Object"
(a) Codd [8] 'introduced the definition of data: DATA: (Object, Attribute, Value). The concept "object" in principle covers here a simple element; the generalised concept "attribute", including linguistical characterisation permits the establishment of "Relational Data Base". The structural features can be formalised by binary algebra and predicate logics. (b) Bouill6 [4] introduced the definition: DATA: (Class, Objects, Attribute, Links) in the set theoretical sense. The concept "object" is generalised to include any "entity". Such a definition permits the establishment of hierarchical structures (hyperclass, class, sub-class) and parallel structures (purpose links). The structural features can be formalised in the framework of Hypergraph theory. (c) In the object oriented language [9,31], the concept "object" is generalised to include "ele-
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ment", "entity", "transformation law", "procedure". This permits to establish semantic and syntactic relations between various levels of knowledge and know-how. Various advanced logics attempt to formalise the structural features of knowledge. Only partial results have been obtained [29]. All the expressions used are easily understandable, satisfying RQ1. The computability is only obtained in simple cases. RQ2 is only partially satisfied. 4.2. Utility 2. Around "'Abstract Types"
In programming languages (C, PL1, Fortran, Pascal), the definition of an abstract type is: AT: (Values, Operators), e.g., integers with arithmetic operators, binary numbers with logical binary operations. Many extensions have been suggested, e.g. (a) Image processing [2]. Values: (elementary geometrical figures); Operators: (construction laws). (b) Icon algebra [6]. Values: (icons); Operators: (manipulators). Generalisation: to design an abstract type is to construct a modelling language, of which the triple (semantics, syntax, pragmatics) satisfies the semiotical property, but only in a specific universe. In that specific universe, all the requirements RQ1 to RQ4 are satisfied. 4.3. Utility 3. "'Predicate Logic and Others"
(a) First order predicate logic. The used formal elements: variables and constants, functions, predicates, terms, clauses are general and understandable. The limits of the generality lie in the defined quantifiers and connectors. The suggested inference laws (modus ponens, modus tolens, if then else) and inference engines (chainings) are in lack of transcendance [7]. Reason 1: the implication connector suffers from many paradoxes. Reason 2: the variety of the environments are not sufficiently taken into account. This modelling language satisfies partially RQ1, RQ2 and RQ4. It does not satisfy RQ3. (b) Other advanced logics [30,29]. General attempts take into account imprecision, incompleteness, fuzziness, relevance conditions, defaults, unclear causes, etc. These modelling techniques at-
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tempt to satisfy RQ3. The used formalisms tend to become more and more obscure. They satisfy neither RQ1, nor RQ4.
4.4. Utility 4. Knowledge Classification Various knowledge classifications have been established. We adopt the following one [18,23,24]: Level 1--Element E: simplest entity, physical or abstract. Level 2--Complex element CE: formed with several E. The connectives may be arithmetic, algebraic, logical, linguistical, vectorial (orthogonal or not). Level 3--Transformation T: Operating law transforming one E (or CE) into another E (or CE) Examples: application, transfer function, morphism in category theory, cause to effect relation. Level 4--Statement S: formed with several T to ascertain a truth. Examples: axiom, theorem, predicate clauses, rules in Expert Systems. Level 5--Procedure PR: formed with several S to solve one problem. Examples: algorithm, metarules in Expert Systems. Level 6--Programme PG: formed with several PR to solve several correlated problems. Example: software in Expert Systems. The used expressions to denote the knowledge are generic. They bear specific names in each modelling language. The 6 levels are intrinsically related in a hierarchical way. They may be represented by a semantic tree. The utility in the PMSDSS design (Fig. 3) is to form a Knowledge Reference Model guiding Knowledge Extraction from
the physical system (PS) and management system (MS) and knowledge programming in the computerised DSS. The characteristic features in practical use are their states or situations (next section). Each piece of knowledge (or state or situation) can be viewed as a generalised object in Godd's sense: knowledge: (state or situation, attribute value).
5. Situation Algebra After analysing the intelligence needs of our PMS problems, and the intelligence lacks of the existing AI techniques, we attempt to construct a satisfactory modelling language.
Story 5. "Yuko and the Hole in the Sky" Billions of years ago, someone decides to separate the sky and the earth. After the big explosion, a hole remains in the sky. Yuko, a very beautiful lady, picks a large inflammed rock, and puts it in the hole. Afterwards, the harmony is established in our universe. The harmony is the expected satisfactory modelling language for the PMS°DSS design. The hole is the AI lacks. The rock is the activity-situation algebra [26]. Basic problem-solving principle: Establish strategies and tactics for qualitative problem-solving first, guide the search of decision-making conditions towards quantitative problem-solving.
5.1. Vocabulary
Definition. SUPM, if)
I PhysicalSystem(PS) ~ ~ r Management System ( M S ) ~ Ex~rElct[on
) Decision-SupportSystem
T
Programming Langages
Fig. 3.
Computers in lndust~
ACTIVITY:
A = (f,
FN,
SUPI,
This is a 5-tuple machine with flows f, functionality behaviour FN, intellectual support SUPI, material ressource support SUPM, final flow ft. This definition covers one activity, one programme of activities. This system concept permits representation in an integrated way the manifestations of all the sub-systems (PS, MS, DSS) included in the PMS.
Definition. SITUATION: SIT = general symbol; SIT(a), SIT(element of A), SIT(KN). This is a generalisation of the concept of state. It contains however a prospective significance.
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The concept state means "what it is". The concept situation means "what it is and what must be done in the future". Examples: bad economic situation of a country, insufficient machine preparation. The characterisation power lies in the appropriate attributes. - Situation of a flow SIT(f), of a support SIT(SUPI) or SIT(SUPM), the attribute is a STATE (a speed of an amount, a type of machine), the values can be measured on various scales: real numbers, binary or multivalent numbers, linguistic expressions. It can be a scalar or a vector. - Situation of an activity SIT(A), the attribute is the potentiality: POT(f, FN, SUPI, SUPM). The values are the potentialities of the component-tuples. The potentiality is to be defined relatively with respect to the activity goal (PS-situation with respect to the production goal, MS-activities with respect to the organisational goal, DSSsituation with respect to the aid-goal).
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(b) Knowledge-appropriation manipulation: APKN. Aim: when knowledge is not appropriate for PS, MS or DSS-activities, find a way to reach appropriate knowledge. This concerns both f, FN, SUPI, SUPM, ff. The operation comprises in principle two steps. Step 1: discover the non-appropriateness in the knowledge. Step 2: collect information to suppress the non-appropriateness. The general formula is: APKN(ATTRIBUTE) : SIT(KN (--,ATTRIBUTE)) REASON(ATTRIBUTE) (KN(ATTRIBUTE)) The various useful attributes are: consistency, completness, necessity, sufficiency non fuzzyness, correct formulation, - correct formalisation.
-
-
-
-
-
5.2. Elements of the Algebra
-
Manipulators: MAP : SIT 1 -* SIT 2 Generalisation of the "'operator" concept. A MAP manipulates situations. (a) Evaluation manipulators (determined to evaluate the results of activities, states of support, functionalities, flows, the importance of events or perturbations, the appropriateness of decisionmaking elements, the qualities of DSS elements). Result evaluation: EVR EVR: (actual ff, desired fir) --* SIT(if) Support evaluation: EVSUP EVSUP: (actual SUP, Programmed SUP)--* SIT(SUP) Element debugger: DEBEL DEBEL: SIT(if) ~ SIT(A) (Find the A-element which influences SIT(if)) Horizon debugger: DEBH DEBH: SIT(if) --, SIT(SUP3, SUP4) (find the right horizon (ST, MT, or LT), the decisional-framework which corresponds to SIT(if)) Branch debugger. DEBBR -
-
-
(c) Actuation manipulators. Aim: modify the situations SIT(A) or SIT(element-A) of PS, MS, DSS-activities for the dynamic adjustments. - DFLOW: SIT(f)1 ~ SIT(f)2 (where f is the flow state from f0 to ff of an activity and fl, f2. . . . . ff is a programme of activities) For PS; {F1, F2, F3}, Fn For MS: I, C For DSS: Q, R - MODSUP: SIT(SUP)I -~ SIT(SUP)2 For PS: SUP1, SUP2 (methods and tools) For MS; SUP3, SUP4: (strategies, tactics), field of actions, frameworks For DSS: SUP5, SUP6: data-structure, rulestructure, procedures, hardwares.
-
-
DEBBR: SIT(ff/BI) --, SIT(A/B J) (find the activity A of some branch BJ which influences SIT(if) of branch BI).
5.3. Application Example Problem: find the reason of a bottleneck occuring in front of a workstation (WS). Procedure for the solution:
Step 1. Determine the related activities: A2: programmed activity on WS, Al: programmed activity preceeding WS;
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Step 2. Evaluate SIT(ff/A1);
if Actual(ff/A1) larger than the programmed value, evaluate ( S I T ( S U P / A 1 ) ; if Actual(ff/A1) smaller or equal, evaluate SIT(SUP/A2); Step 3. If S I T ( S U P / A 2 ) smaller than the programmed value, make A P K N ( S I T ( A 3 / MT) to find complete knowledge on medium-horizon planning; Determine S I T ( A 3 / M T ) ; Step 4. Evaluate decisions on S U P / A 3 / M T (possible actions, i.e.: decision made on W S / A 2 capacities); If the planned values are too small, make (MODSUP(WS): S I T ( S U P / A 2 ) a c t u a l SIT(SUP/A2)better. The situational algebra underlies the following problem solving principle: establish strategies and tactics for qualitative problem solving firstly, guide the search of decision making conditions for quantitative problem solving. This principle is derived from the following considerations. A production management is perfect, if, for all branches of activities and for all horizons (long, medium, short), we succeed to make: (a) feasible planning, where goals and activities are p r o g r a m m e d in executable details, where intellectual and material supports are predetermined coherently; (b) efficient supervising, where dispatchings, results, supports, interrelations are well coordinated and well synchronised; (c) appropriate dynamic adjustings, where unpredicted perturbations can be debugged and where support margins and time margins are sufficiently large so that correcting actions are possible. (This situation is reached in kanban applications, because in line-type manufacturing where conditions are well defined, there is consensus a m o n g the human executors to achieve coordination). In practice, this ideal situation is rarely reached, because: (a) Internal and external capacities are not well known in advance, so the planning is not always feasible. (b) communication between executors suffers f r o m not being precise, individualism and ignorance, so global supervision is not always efficient.
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(c) few margins are installed preventively in the flow control and the support utilisation (because this is expensive), so the dynamic adjustings are not appropriate.
6. Conclusion Story 6. "The Cat and the Fish"
One day, the young David attaches a b a m b o o stick on the back of the cat Sultan. At the high extremity of the stick, a nice little fish is fixed. Sultan spreads his claws and tries to catch the fish above his head, but cannot reach it. Sultan j u m p s higher. H e climbs the steps, reaches the terrasse on the roof. A big j u m p and he falls on the ground. He reaches again the steps, he c l i m b s . . . Five thousand year ago, Wu-Shi has already explained in Yi-King, the nature of this Y i n g - Y a n g cycle. In this paper, we have only made one jump.
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