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Decision simulation (DSIM) - one outcome of combining expert systems and decision support systems
Artificial Intelligence in Economics and Management L.F. Pau (Editor) ©Elsevier Science Publishers B.V. (North-Holland), 1986
DECISION SIMULATION (DSIM) - ONE OUTCOME OF COMBINING EXPERT SYSTEMS AND DECISION SUPPORT SYSTEMS Kenneth J. Fordyce Gerald A. Sullivan International Business Machines Corp. Poughkeepsie. NY 12602, 12602. U.S.A.
This paper outlines one role for expert systems (ES) in decision support systems (DSS). This combination results in a hybrid system called Decision Support / Decision Simulation (DSIM). Examples are presented. INTRODUCTION In the past year reporting in the popular press has improved the general availability of facts about artificial intelligence (AI). The image being generated portrays AI as a powerful new t09L. Like other tools, topl. tools. AI allows a human to easily control contfol and direct power sources in accomplishment of a task by providing cognitive amplification or augmentation. Research in AI generally falls into one of two major categories: (1) making machines more useful to humans, humans. and (2) understanding intelligence. Since we are practitioners with interest and experience in implementations of AI which funcfun~ tion as tools in support of the decision maker, the focus of this paper is 'increased usefulness'. Specifically, the implic~tions implicntions that expert systems (ES) subarea of AI has in the arena of decision support systems (DSS). The paper will proceed as follows: A review of computer based decision support tools. A working definition of ES. A description of the role of ES in DSS and and a definition of Decision Support / Decision Simulation (DSIM). Some examples of expert systems being developed by us. Finally, an estimation of what can be accomplished in the future. Our work is done in APL (Sullivan and Fordyce 1985). COMPUTER-BASED DECISION SUPPORT TOOLS OR AIDS One primary use of computers is to support decision making at all levels of an organization. Over the past 30 years a number of distinct "software technologies" have developed to provide decision support tools or aids by "harnessing" the computer. Builders of application systems to provide decis~pport integrate specific tools from each of the sion support technologies to meet the needs of the group of decision makers they are servicing. Expert or knowledge based systems
31
32
KJ. Fordyce Fordyce and G.A. C.A. Sullivan Sullivan K.J.
can be viewed as one of the newest decision support software technology. These technologies are: 1.
Base Query Data Base in this group enable the user to easily easily and flexiTools in bility retrieve factual information. The information real-time. or future projections. may be historical. real-time. are divided divided into Into two components: aa class .and . and a Facts are specific instance. For example. price would be class of facts (the variable name). and $8.50 would be a specific value of the variable for for a certain situinstance (the value ation). The facts are organized The organized into into data bases. relationships among the facts is used to structure the data base.
2.
Normative Models Tools in this group provide the user a recommended action or generate new knowledge. The facts are organor differential equations. equations. ized into a set of algebraic or Many of these tools come from operations research. staMany tistics. and engineering. For example. statistical techniques might provide a confidence interval or a facts. linear programming regression model from a set of facts. might tell the user how to distribute his product among four warehouses. and an engineering model might tell the kiln or or what user at what temperature to operate his kiln size furnace to install. size
3.
Simulation Tools in this group enable the user to project project the outcome of specific set of actions. actions . This This is called "what if" analysis. The facts are organized into a network which represents the actual flow of events. Flowcharts initially represent represent the flow. are most often used to initially flow is The simulation is made operational when the flow encoded into computer code. Simulation can be split management science and into three groups: engineering. management planning . financial planning.
4.
Visual Aids help the user understand the the inforTools in this group help mation by by providing providing a variety of graphical or or pictorial mation (icons) representations of the information.
S. 5.
Networks Tools in this group help the user obtain information information Tools from another computer system or send Information to another information system.
6.
Expert or Knowledge-Based Systems Tools in this group provide the user a recommended action or generate new knowledge. knowledge. action This is the same as normative normative models. but the methods used used are function as much different. and the problems addressed are usually
Decision Simulation (DSIM)
those for which modeling is inappropriate. The facts usually organized into rules. frames. and/or netare usuallY works. The logical relationships among the facts as understood by a human expertCs) is used to build these structures. This collection of information is called a knowledge base. Some type of "reasoning mechanism" is used to extract the appropriate facts from the knowledge base to satisfy a request. This reasoning mechanism usually has the ability to explain why it recommended a usuallY certain action. REVIEW OF EXPERT SYSTEMS field. There are a number of sub-specialities within the AI fielrl. They include: vision. Robotics. speech recognition. human problem solving, natural language interfaces, interfaces. and knowledge-based expert systems. systems . The last two have the most immediate potential impact on decision support systems. The goal of the artificial intelligence (AI) field is to develop computational approaches to intelligent behavior (Gevarter 1983). Some of the earliest work done in the AI field got computers to play games like chess and checkers. Of particular importance is getting machines to solve problems or carry out tasks by enabling them to purposefully manipulate s~mbols, s~mbols. recognize and appropriately respond to patterns, patterns. and/or learn or adapt in a manner similar to human beings. Additionally, Additionally. AI includes the productive merging of symbol manipulation manipUlation and pattern recognition by the computer, computer. with the computers ability to carry out extensive computational algorithms quickly and accurately. Knowledge-based expert systems (ES) is defined CRauch 1984) to be a class of computer programs intended to serve as consultants for decision making. These programs use a collection of facts, rules of thumb, thumb. and other knowledge about a limited field to help make inferences in the field. They differ substantially from conventional computer programs in that their goals may have no algorithmic solution, and they must make inferences based on incomplete or uncertain inforc~lled expert systems because they address mation. They are called problems normally thought to require human specialists for solution, solution. and knowledge-based because researchers have found that amassing a large amount of knowledge. rather than sophisticated reason techniques, techniques. is responsible for the success of the A predecessor of knowledge-based the approach. expert expert systems is is decision tables ( Decision Tables 1978). The process process of of building building an expert system system is is called knowledge engineering. Knowledge engineering. Knowledge engineering is aa subarea of of problem situation analysis (Sullivan and The and Fordyce Fordyce 1983). The following ing drawing drawing depicts depicts the general makeup make up of a rule-based rule-based expert expert system. system.
33
K.J. Fordyce and G.A. Sullivan
34
KNOWLEDGE BASE 1 rules for the 1 problem domain
1 1<--1 answers 1<--1 USER 1 1 ,md 1--->1 explanation 1 1
1
----------
1<--I EXPERT 1 1 ----------
INFERENCE MECH~NISM MECf-Il'INISM
DATA BASE 1 -------------1 specific facts 1<--1 data 1 I 1 or observations 1 1I collection 1I
Knowledge-based expert systems and techniques enable information system builders to move problem domain knowledge Benefits can information from the human to the computer. accrue to an organization in four ways (Rychener 1985): 1. 2. 3. 4.
Provide expertise when human expertise is not available Provide expertise more uniformly, and sometimes faster Assist experts in making decisions in complex situations Characterize the problem domain
The expert systems of the 1980's now function productively in such diverse areas as medical diagnosis, mineral & oil exploration. locating. exploration, tactical targeting, equipment fault locating, computer operation and configuration, and management of manu f act u r i n g 0operatf~ns. per at fo n s • Sullivan Sui I i van and For d y c e (1984) (1 984) has a ufacturing Fordyce more detailed description of expert systems. ES AND DSS To understand how the ES and DSS combination can help the decision maker. maker, we have to characterize the role. Typically, the decision maker is faced with the need to select from a stream of alternatives by some deadline, those actions which maximize the chance of success and minimize the chance of failure. Success can be something as mundane as increasing corporate market share, or as humane as saving a patient's life. In both cases there are alternative ways to attack the problem. Usually there is a mass of data available. The decision window may be such that: the time needed to reduce the data to information relevant to the decision. decision, exceeds the time for which the opportunity to act exists.
Decision Simulatio Simulation (DSIM) Decision n (DSIM)
Present decision decision support support techniques techniques can can help help assemble assemble the the Present data data into into information, information, if if the the decision decision maker maker speaks speaks its its lanlanguage. Then Then the the information information can can be be used used by by the the decision decision guage. maker Last, maker to to generate generate aa series series of of alternatives alternatives available. available. Last, the the decision decision maker maker can can use use aa simulation simulation language language to to project project the outcome outcome of of each each decision. decision. In many many cases cases the the patient patient has has the In probably probably died, died, or or didn't didn't need need treatment treatment to to survive survive in in the the first place. place. first In situations situations where where the the decision decision window window is is too too small small to to thothoIn roughly analyze analyze aa problem problem with with today's today's support support technology, technology, roughly the decision decision maker maker relies relies on on his his experience experience with with similar similar the situations in in the the past. past. This This experience experience base base allows allows the the situations decision decision maker maker to to rapidly rapidly sort sort relevant relevant input input from from non-relevant, to to rapidly rapidly arrive arrive at at aa choice, choice, and and take take action action non-relevant, on that that basis. basis. The The experience experience base base is is difficult difficult and and costly costly on to build. build. It It is is not not easily easily transferable transferable from from one one mind mind to to to another. Thus Thus experts experts tend tend to to be be in in short short supply. supply. another. For aa computer computer to to fully fully augment augment the the decision decision maker's maker'S experexperFor tise tise two two conditions conditions are are necessary: necessary: 1. 1.
2. 2.
The computer computer must must be be made made to to understand understand the the data data and and The problem well well enough enough to to reduce reduce the the flow flow to to aa structured structured problem series of of the the available available alternatives alternatives with with an an explanation explanation series of their their consequence consequence of The language language barrier barrier must must be be eliminated eliminated The
The first first ~ondition condition is is something something ES ES can can help help with. with. Natural Natural The language interfaces interfaces (NLI) (NLI) can can help help with with the the second. second. language Until recently, recently, system system applications applications which which aimed aimed at at helping helping Until the decision decision maker maker have have been been bound bound to to traditional traditional computacomputathe tional methods. methods. The actions actions taken taken by by the the computer computer being being tional The limited to to only only those those which which the the systems systems analysts analysts and and proprolimited grammers anticipated anticipated might might be be necessary. necessary. grammers Current Current techniques techniques provide provide for for gathering gathering the the expertise expertise of of successful decision decision makers makers and and characteristics characteristics of of the the envienvisuccessful ronment, and and then then making making it it available available in in an an untiring, untiring, conconronment, stantly available, available, and and adaptable adaptable (non-procedural) (non-procedural) form form stantly called the the expert expert system. system. The The domain domain knowledge knowledge provided provided by by called expert expert systems systems will will add add structure structure to to the the data. data. Such Such systems systems can assist assist in in finding finding the the appropriate appropriate computational computational algoalgocan rithm rithm or or data data set, set, aa solution solution for for problems problems where where the the computational computational algorithm algorithm alone alone is is not not sufficient sufficient and and explain explain its its reasoning. reasoning. Additionally, Additionally, current current work work in in natural natural language language interfaces interfaces and nested nested windows windows significantly significantly ease ease communication communication between between and humans and and machines. machines. humans Expert systems systems are are one one of of the the key key tools tools in in moving moving DSS DSS into into Expert the next next step step in in the the evolution evolution from from passive passive data data storage storage to to the
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G. A. Sullivan K.J. Fordyce and G.A.
highly active systems that participate in the decision making process. Figure 1 contains the hierarchy. This last process . step is called Decision Support / Decision Simulation (DSIM), In DS~M (DSIM). DS1M data is increasingly extended into more compact and useful information. DSIM combines traditional DSS, simulation, data DSS. quantitative analysis, analysis. graphics and simulation. base management and query, and expert systems, It intesystems. grates all six decision support technologies to provide the decision maker a more powerful decision aid. ACTIVE
INFORMATION
I
I DSIM - DECISION SUPPORT / SIMULATION DSS - DECISION SUPPORT SYSTEMS MIS - MANAGEMENT INFORMATION SYSTEMS DB - DATA BASES
I
I
PASSIVE
DATA Figure 1 Management System Hierarchy
DSIM is not limited to answering the questions: What is the present status of some actjvity, activity, or what if I change the value assigned to a variable in a model? DSIM is qble ~ble to formulate alternatives, predict logical outcomes, and answer the question why. why . Many of the new support systems are moving in the DSIM direction. For e~ample Kosy (1984), Fox (1984), Brown (1985), Bard (1985), Wiig (1985), Hagaman (1985), and Duchessi (1985), This direction is also reflected in some (1985). the latest expert system shells, languages, and consulting businesses. Examples are Inference Corporation's ART APL2, many of the (1984), Intellicorp's KEE (1984), IBM's APL2. recent enhancements made to LISP and PROLOG. LOGISTICS MANAGEMENT SYSTEMS The manufacturing environment is one of constant flux. Rarely do short-term operational assumptions hold true long enough to be implemented completely. Recovery and redicompletelY. rection are more the rule than the exception. A system for logistics management is a closely coupled clustering of decision support systems (including expert system components) to aid the decision maker in coping with this environment. As is the case with decision support systems in general, this class of system assists the decision maker by providing structure to the rather unstructured problems facing him in managing a large scale logistics network like the ones found in major manufacturing operations.
Decisio n Simulation Simulatioll (DSIM)
Logistics is controlling the manner in which the manufacturing process unfolds. The floor manager must get the right parts to the right place on the right tool at the right time. There are wide variety of unique ffinal i nal assemblies. Some achieve their uniqueness from the processes used to construct them, others by the components from which they are assembled. Each follows a unique path through the process flow. Current hi-tech manufacturers are dealing with processes whose control tolerances are expressed in microns and nanoseconds. Required processes and tooling are often so technologically advanced as to be on the very edge of possibility. These factors combine to form the antithesis of the predictable operating environment. Thus, although the problems encountered by the decision maker are composed of a set of basic parts, their variety is limitless. Expert systems have the ability to approach the problem by machine application of codified knowledge obtained from experts in floor control and management. This rules base is coupled with data reflecting the current state of the line. The actual state is compared to the planned, or desired state. If a variance exists between the two, the system s i tuation. uses the rules to develop a response to the situation. A logistics management systems would, include the following components: - Status repeater and alert mechanism. A cluster of rules determine when a particular happening on the line should i have attention directed towards it. It also determines when attention to the problem should be escalated. This information is made available to the decision maker through animated graphics and windows. - Log Analysis After evaluating the the log maintained by roving inspect, this component makes recommendations about altering the inspection strategy. strategy . - Recovery Generator This component will recommend actions to decision makers when a variance from plan has occurred. For example, if a process fails, a machine goes down, or the queue builds up past acceptable limits. - Review of past and present states of line activity. Using graphical information representation (animated modeling), portraYs portrays various levels of activity in the line. - Future projection of the line. A manager can input a recovery plan he devised in response to a current problem and then run the projection to see the probable outcome of his proposed reaction to the problem. In developing the projection, the system will simulate the activity of the entire system to portray interactions i nteractions of scheduling, WIP, internode impacts, goals met, and goals missed.
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K.J. Fordyce and G.A. G.A . Sullivan Sul/ivan
The first purpose of a logistics management system is to enable various levels of management to view the manufacturing floor in real time without having to physically visit it. The second is to help cope with the complexity and size of the operation being managed. Such a system would be a hybrid of a number of expert subsystems and several traditionally procedural modules running under a software bus architecture. Survey of the literature has established that there is a need for more support in logistics management than currently exists. It is not a matter of gaining expertise. The needed expertise exists today. The problem is growing more unmanageable because the increase in need for this expertise is coupled with competitive pressure to cut cost by reducing the number of people on hand to apply it. Expert systems evidence great potential to handle this situation. Integration of traditional manufacturing control systems with adaptive systems capable of functioning at an expert floor manager level is the premise on which logistic management systems are predicated. AUTOMATED LEDGER BOOK SOOK (ALB) (ALS) ALB is a worksheet model generator with an equation solving / analyzing The analyst mo"de'ls anal,zing expert system component. mo~e)s the problem using algebraic notation to describe the relationships that exist between the variables involved. These relationships, or rules, make up the flow of the analysis. The system then employs an algebra inference engine coupled with the rules of algebra to understand the structure of the "ALS will wi 11 develop analysis. Depending on the data involved, "ALB an execution strategy for resolving the relationships. The order will be dependent on the data and structure, structure. but independent of any procedural implications. The problem is always resolved in the minimum number of cycles. A full explanation is always available. Let's look at an example. A farm manager wants to calculate profit and cost ratio for his apple crop. The equations in his model are: EQ EQ EQ EQ EQ EQ
1 : REYEN SOLD x PRICE REVEN 2 : VCOST = SEED + FERT + LABOR MACHO 3 : FCOST MACHD + TAX
4: EXPEN YCOST VCOST + FCOST 5: PROFT = REVEN - EXPEN 6 : RATIO = EXPEN / SOLD
Initially the user wants the output variables calculated, but then he may want to specify an output variable, variable. and have an input variable calculated. For example, example. the farm manager may wish to calculate PROFT, SOLD, PRICE, MACHD, MACHO. and FCOST
39
Decision Decisio n Simulation (DSIM) (DS/M)
based on REVEN, EXPEN, RATIO, SEED, FERT, LABOR, LASOR, VCOST, and TAX. ALS will determine the following sequence of calculations ALB will find PROFT, SOLD, PRICE, MACHO, and FCOST without any new instructions from the user.
- solve equation
-
solve solve solve solve - solve
-
equation equation _equat i on equation equation equation
VCOST 2 for VeOST FCOST 4 for FeOST 3 for MACHO 5 for PROFT (; 6
I
for SOLD for PRICE
Another question the farm crop manager might have is: the input variables effecting profit"
"What
ALB ALS would provide the following answer: PROFT == REVEN EXPEN REVEN = SOLD x PRICE EX PEN = VCOST + FeOST FCOST EXPEN VCOST SEED ++ FERT + LABOR LASOR FCOST = MACHO + TAX - PROFIT IS A FUNCTION OF SOLD PRICE SEED FERT
LABOR LASOR
MACHO
TAX
CONCLUSIONS Today's technology can and does measurably support the decision maker. Exploitation of AI in DSS/DSIM OSS/OSIM is largely a matter of implementation. It is academia's role to produce people capable of understanding, building and using what technology provides. It is industry's role to develop and use thQ tha tools responsibly responsibly..
40
K.J. Fordyce and G.A. C.A. Sullivan
REFERENCES Bard, V., Y., "APlPIE - An APl Programmed Inference Engine," IBM Cambridge. Scientific Center, Cambridge, MA 02142, 1985. Brown, J., Cook, J., Groner, l., l . , and Eusebi, E., "logic Programming in APl2," IBM Santa Teresa lab, San JosP, Calif. 95150, 1985. Decision Tables: A SYstems Systems Analysis and Documentation Tech.!l..iru!J:. .!l.i...9..!!J: (2nd ed.), IBM, White Plains, NY., NV., 1978. Duchessi, P., "The Conceptual Design of a Knowledge-Based System for Aggregate Planning," School of Business, Suny at Albany, Albany NY 12222, 1985. 1985 . Fox, M. and Smith, S., "ISIS - a Knowledge Based System for Smith,S., Factory Scheduling," Expert Systems. vol. 1, Systems . I, no. I, pp. 25-50, July 1984. Gevarter, W., "Expert Systems: limited But Powerful," lIS ~ Spectrum, pp. 39-45, August 1983. Hagaman, W. et al., "MEDCAT: An APl Program for Medical Diagnosis, Consultation, and Teaching," Proceedings of the 1985 Conference. Kosy, D. and Wise, B. "Self-Explanatory Financial Planning Models," Proceedings of the 1984 National Conference on o~ Artificial Intelligence. Rauch, H. "Probability Concepts for an Expert System Used for Data Fusion." AI ~'a9azine. vol. 5, no. 3, pp. Magazine. 55-60, Fall 1984. Rychener, M., "Expert Systems for Engineering Design," Expert Systems. pp. 30-44, vol. 2, no. I, January 1985. Sullivan, G. and Fordyce, K., "A Decision Support System Paradigm." IBM, H20/906, Poughkeepsie, NV NY 12602, 1983. Sullivan, G. and Fordyce, K., "A Review of Expert Systems," IBM, H20/906, Poughkeepsie, NY 12602,1984. 12602, 1984. Sullivan, G. and Fordyce, K., "AIDA: Artificial Development Aids for APl," Proceedings of the 1985 APl Conference. Wiig, K., K. , "The Arthur D. little Artificial Intelligence Applications Center," Cambridge, MA 02140, 1985.
DECISION SIMULATION (DSIM) - ONE OUTCOME OF COMBINING EXPERT SYSTEMS AND DECISION SUPPORT SYSTEMS Kenneth J. Fordyce Gerald A. Sullivan International Business Machines Corp. Poughkeepsie. NY 12602, 12602. U.S.A.
This paper outlines one role for expert systems (ES) in decision support systems (DSS). This combination results in a hybrid system called Decision Support / Decision Simulation (DSIM). Examples are presented. INTRODUCTION In the past year reporting in the popular press has improved the general availability of facts about artificial intelligence (AI). The image being generated portrays AI as a powerful new t09L. Like other tools, topl. tools. AI allows a human to easily control contfol and direct power sources in accomplishment of a task by providing cognitive amplification or augmentation. Research in AI generally falls into one of two major categories: (1) making machines more useful to humans, humans. and (2) understanding intelligence. Since we are practitioners with interest and experience in implementations of AI which funcfun~ tion as tools in support of the decision maker, the focus of this paper is 'increased usefulness'. Specifically, the implic~tions implicntions that expert systems (ES) subarea of AI has in the arena of decision support systems (DSS). The paper will proceed as follows: A review of computer based decision support tools. A working definition of ES. A description of the role of ES in DSS and and a definition of Decision Support / Decision Simulation (DSIM). Some examples of expert systems being developed by us. Finally, an estimation of what can be accomplished in the future. Our work is done in APL (Sullivan and Fordyce 1985). COMPUTER-BASED DECISION SUPPORT TOOLS OR AIDS One primary use of computers is to support decision making at all levels of an organization. Over the past 30 years a number of distinct "software technologies" have developed to provide decision support tools or aids by "harnessing" the computer. Builders of application systems to provide decis~pport integrate specific tools from each of the sion support technologies to meet the needs of the group of decision makers they are servicing. Expert or knowledge based systems
31
32
KJ. Fordyce Fordyce and G.A. C.A. Sullivan Sullivan K.J.
can be viewed as one of the newest decision support software technology. These technologies are: 1.
Base Query Data Base in this group enable the user to easily easily and flexiTools in bility retrieve factual information. The information real-time. or future projections. may be historical. real-time. are divided divided into Into two components: aa class .and . and a Facts are specific instance. For example. price would be class of facts (the variable name). and $8.50 would be a specific value of the variable for for a certain situinstance (the value ation). The facts are organized The organized into into data bases. relationships among the facts is used to structure the data base.
2.
Normative Models Tools in this group provide the user a recommended action or generate new knowledge. The facts are organor differential equations. equations. ized into a set of algebraic or Many of these tools come from operations research. staMany tistics. and engineering. For example. statistical techniques might provide a confidence interval or a facts. linear programming regression model from a set of facts. might tell the user how to distribute his product among four warehouses. and an engineering model might tell the kiln or or what user at what temperature to operate his kiln size furnace to install. size
3.
Simulation Tools in this group enable the user to project project the outcome of specific set of actions. actions . This This is called "what if" analysis. The facts are organized into a network which represents the actual flow of events. Flowcharts initially represent represent the flow. are most often used to initially flow is The simulation is made operational when the flow encoded into computer code. Simulation can be split management science and into three groups: engineering. management planning . financial planning.
4.
Visual Aids help the user understand the the inforTools in this group help mation by by providing providing a variety of graphical or or pictorial mation (icons) representations of the information.
S. 5.
Networks Tools in this group help the user obtain information information Tools from another computer system or send Information to another information system.
6.
Expert or Knowledge-Based Systems Tools in this group provide the user a recommended action or generate new knowledge. knowledge. action This is the same as normative normative models. but the methods used used are function as much different. and the problems addressed are usually
Decision Simulation (DSIM)
those for which modeling is inappropriate. The facts usually organized into rules. frames. and/or netare usuallY works. The logical relationships among the facts as understood by a human expertCs) is used to build these structures. This collection of information is called a knowledge base. Some type of "reasoning mechanism" is used to extract the appropriate facts from the knowledge base to satisfy a request. This reasoning mechanism usually has the ability to explain why it recommended a usuallY certain action. REVIEW OF EXPERT SYSTEMS field. There are a number of sub-specialities within the AI fielrl. They include: vision. Robotics. speech recognition. human problem solving, natural language interfaces, interfaces. and knowledge-based expert systems. systems . The last two have the most immediate potential impact on decision support systems. The goal of the artificial intelligence (AI) field is to develop computational approaches to intelligent behavior (Gevarter 1983). Some of the earliest work done in the AI field got computers to play games like chess and checkers. Of particular importance is getting machines to solve problems or carry out tasks by enabling them to purposefully manipulate s~mbols, s~mbols. recognize and appropriately respond to patterns, patterns. and/or learn or adapt in a manner similar to human beings. Additionally, Additionally. AI includes the productive merging of symbol manipulation manipUlation and pattern recognition by the computer, computer. with the computers ability to carry out extensive computational algorithms quickly and accurately. Knowledge-based expert systems (ES) is defined CRauch 1984) to be a class of computer programs intended to serve as consultants for decision making. These programs use a collection of facts, rules of thumb, thumb. and other knowledge about a limited field to help make inferences in the field. They differ substantially from conventional computer programs in that their goals may have no algorithmic solution, and they must make inferences based on incomplete or uncertain inforc~lled expert systems because they address mation. They are called problems normally thought to require human specialists for solution, solution. and knowledge-based because researchers have found that amassing a large amount of knowledge. rather than sophisticated reason techniques, techniques. is responsible for the success of the A predecessor of knowledge-based the approach. expert expert systems is is decision tables ( Decision Tables 1978). The process process of of building building an expert system system is is called knowledge engineering. Knowledge engineering. Knowledge engineering is aa subarea of of problem situation analysis (Sullivan and The and Fordyce Fordyce 1983). The following ing drawing drawing depicts depicts the general makeup make up of a rule-based rule-based expert expert system. system.
33
K.J. Fordyce and G.A. Sullivan
34
KNOWLEDGE BASE 1 rules for the 1 problem domain
1 1<--1 answers 1<--1 USER 1 1 ,md 1--->
1
----------
1<--I EXPERT 1 1 ----------
INFERENCE MECH~NISM MECf-Il'INISM
DATA BASE 1 -------------1 specific facts 1<--1 data 1 I 1 or observations 1 1I collection 1I
Knowledge-based expert systems and techniques enable information system builders to move problem domain knowledge Benefits can information from the human to the computer. accrue to an organization in four ways (Rychener 1985): 1. 2. 3. 4.
Provide expertise when human expertise is not available Provide expertise more uniformly, and sometimes faster Assist experts in making decisions in complex situations Characterize the problem domain
The expert systems of the 1980's now function productively in such diverse areas as medical diagnosis, mineral & oil exploration. locating. exploration, tactical targeting, equipment fault locating, computer operation and configuration, and management of manu f act u r i n g 0operatf~ns. per at fo n s • Sullivan Sui I i van and For d y c e (1984) (1 984) has a ufacturing Fordyce more detailed description of expert systems. ES AND DSS To understand how the ES and DSS combination can help the decision maker. maker, we have to characterize the role. Typically, the decision maker is faced with the need to select from a stream of alternatives by some deadline, those actions which maximize the chance of success and minimize the chance of failure. Success can be something as mundane as increasing corporate market share, or as humane as saving a patient's life. In both cases there are alternative ways to attack the problem. Usually there is a mass of data available. The decision window may be such that: the time needed to reduce the data to information relevant to the decision. decision, exceeds the time for which the opportunity to act exists.
Decision Simulatio Simulation (DSIM) Decision n (DSIM)
Present decision decision support support techniques techniques can can help help assemble assemble the the Present data data into into information, information, if if the the decision decision maker maker speaks speaks its its lanlanguage. Then Then the the information information can can be be used used by by the the decision decision guage. maker Last, maker to to generate generate aa series series of of alternatives alternatives available. available. Last, the the decision decision maker maker can can use use aa simulation simulation language language to to project project the outcome outcome of of each each decision. decision. In many many cases cases the the patient patient has has the In probably probably died, died, or or didn't didn't need need treatment treatment to to survive survive in in the the first place. place. first In situations situations where where the the decision decision window window is is too too small small to to thothoIn roughly analyze analyze aa problem problem with with today's today's support support technology, technology, roughly the decision decision maker maker relies relies on on his his experience experience with with similar similar the situations in in the the past. past. This This experience experience base base allows allows the the situations decision decision maker maker to to rapidly rapidly sort sort relevant relevant input input from from non-relevant, to to rapidly rapidly arrive arrive at at aa choice, choice, and and take take action action non-relevant, on that that basis. basis. The The experience experience base base is is difficult difficult and and costly costly on to build. build. It It is is not not easily easily transferable transferable from from one one mind mind to to to another. Thus Thus experts experts tend tend to to be be in in short short supply. supply. another. For aa computer computer to to fully fully augment augment the the decision decision maker's maker'S experexperFor tise tise two two conditions conditions are are necessary: necessary: 1. 1.
2. 2.
The computer computer must must be be made made to to understand understand the the data data and and The problem well well enough enough to to reduce reduce the the flow flow to to aa structured structured problem series of of the the available available alternatives alternatives with with an an explanation explanation series of their their consequence consequence of The language language barrier barrier must must be be eliminated eliminated The
The first first ~ondition condition is is something something ES ES can can help help with. with. Natural Natural The language interfaces interfaces (NLI) (NLI) can can help help with with the the second. second. language Until recently, recently, system system applications applications which which aimed aimed at at helping helping Until the decision decision maker maker have have been been bound bound to to traditional traditional computacomputathe tional methods. methods. The actions actions taken taken by by the the computer computer being being tional The limited to to only only those those which which the the systems systems analysts analysts and and proprolimited grammers anticipated anticipated might might be be necessary. necessary. grammers Current Current techniques techniques provide provide for for gathering gathering the the expertise expertise of of successful decision decision makers makers and and characteristics characteristics of of the the envienvisuccessful ronment, and and then then making making it it available available in in an an untiring, untiring, conconronment, stantly available, available, and and adaptable adaptable (non-procedural) (non-procedural) form form stantly called the the expert expert system. system. The The domain domain knowledge knowledge provided provided by by called expert expert systems systems will will add add structure structure to to the the data. data. Such Such systems systems can assist assist in in finding finding the the appropriate appropriate computational computational algoalgocan rithm rithm or or data data set, set, aa solution solution for for problems problems where where the the computational computational algorithm algorithm alone alone is is not not sufficient sufficient and and explain explain its its reasoning. reasoning. Additionally, Additionally, current current work work in in natural natural language language interfaces interfaces and nested nested windows windows significantly significantly ease ease communication communication between between and humans and and machines. machines. humans Expert systems systems are are one one of of the the key key tools tools in in moving moving DSS DSS into into Expert the next next step step in in the the evolution evolution from from passive passive data data storage storage to to the
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G. A. Sullivan K.J. Fordyce and G.A.
highly active systems that participate in the decision making process. Figure 1 contains the hierarchy. This last process . step is called Decision Support / Decision Simulation (DSIM), In DS~M (DSIM). DS1M data is increasingly extended into more compact and useful information. DSIM combines traditional DSS, simulation, data DSS. quantitative analysis, analysis. graphics and simulation. base management and query, and expert systems, It intesystems. grates all six decision support technologies to provide the decision maker a more powerful decision aid. ACTIVE
INFORMATION
I
I DSIM - DECISION SUPPORT / SIMULATION DSS - DECISION SUPPORT SYSTEMS MIS - MANAGEMENT INFORMATION SYSTEMS DB - DATA BASES
I
I
PASSIVE
DATA Figure 1 Management System Hierarchy
DSIM is not limited to answering the questions: What is the present status of some actjvity, activity, or what if I change the value assigned to a variable in a model? DSIM is qble ~ble to formulate alternatives, predict logical outcomes, and answer the question why. why . Many of the new support systems are moving in the DSIM direction. For e~ample Kosy (1984), Fox (1984), Brown (1985), Bard (1985), Wiig (1985), Hagaman (1985), and Duchessi (1985), This direction is also reflected in some (1985). the latest expert system shells, languages, and consulting businesses. Examples are Inference Corporation's ART APL2, many of the (1984), Intellicorp's KEE (1984), IBM's APL2. recent enhancements made to LISP and PROLOG. LOGISTICS MANAGEMENT SYSTEMS The manufacturing environment is one of constant flux. Rarely do short-term operational assumptions hold true long enough to be implemented completely. Recovery and redicompletelY. rection are more the rule than the exception. A system for logistics management is a closely coupled clustering of decision support systems (including expert system components) to aid the decision maker in coping with this environment. As is the case with decision support systems in general, this class of system assists the decision maker by providing structure to the rather unstructured problems facing him in managing a large scale logistics network like the ones found in major manufacturing operations.
Decisio n Simulation Simulatioll (DSIM)
Logistics is controlling the manner in which the manufacturing process unfolds. The floor manager must get the right parts to the right place on the right tool at the right time. There are wide variety of unique ffinal i nal assemblies. Some achieve their uniqueness from the processes used to construct them, others by the components from which they are assembled. Each follows a unique path through the process flow. Current hi-tech manufacturers are dealing with processes whose control tolerances are expressed in microns and nanoseconds. Required processes and tooling are often so technologically advanced as to be on the very edge of possibility. These factors combine to form the antithesis of the predictable operating environment. Thus, although the problems encountered by the decision maker are composed of a set of basic parts, their variety is limitless. Expert systems have the ability to approach the problem by machine application of codified knowledge obtained from experts in floor control and management. This rules base is coupled with data reflecting the current state of the line. The actual state is compared to the planned, or desired state. If a variance exists between the two, the system s i tuation. uses the rules to develop a response to the situation. A logistics management systems would, include the following components: - Status repeater and alert mechanism. A cluster of rules determine when a particular happening on the line should i have attention directed towards it. It also determines when attention to the problem should be escalated. This information is made available to the decision maker through animated graphics and windows. - Log Analysis After evaluating the the log maintained by roving inspect, this component makes recommendations about altering the inspection strategy. strategy . - Recovery Generator This component will recommend actions to decision makers when a variance from plan has occurred. For example, if a process fails, a machine goes down, or the queue builds up past acceptable limits. - Review of past and present states of line activity. Using graphical information representation (animated modeling), portraYs portrays various levels of activity in the line. - Future projection of the line. A manager can input a recovery plan he devised in response to a current problem and then run the projection to see the probable outcome of his proposed reaction to the problem. In developing the projection, the system will simulate the activity of the entire system to portray interactions i nteractions of scheduling, WIP, internode impacts, goals met, and goals missed.
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K.J. Fordyce and G.A. G.A . Sullivan Sul/ivan
The first purpose of a logistics management system is to enable various levels of management to view the manufacturing floor in real time without having to physically visit it. The second is to help cope with the complexity and size of the operation being managed. Such a system would be a hybrid of a number of expert subsystems and several traditionally procedural modules running under a software bus architecture. Survey of the literature has established that there is a need for more support in logistics management than currently exists. It is not a matter of gaining expertise. The needed expertise exists today. The problem is growing more unmanageable because the increase in need for this expertise is coupled with competitive pressure to cut cost by reducing the number of people on hand to apply it. Expert systems evidence great potential to handle this situation. Integration of traditional manufacturing control systems with adaptive systems capable of functioning at an expert floor manager level is the premise on which logistic management systems are predicated. AUTOMATED LEDGER BOOK SOOK (ALB) (ALS) ALB is a worksheet model generator with an equation solving / analyzing The analyst mo"de'ls anal,zing expert system component. mo~e)s the problem using algebraic notation to describe the relationships that exist between the variables involved. These relationships, or rules, make up the flow of the analysis. The system then employs an algebra inference engine coupled with the rules of algebra to understand the structure of the "ALS will wi 11 develop analysis. Depending on the data involved, "ALB an execution strategy for resolving the relationships. The order will be dependent on the data and structure, structure. but independent of any procedural implications. The problem is always resolved in the minimum number of cycles. A full explanation is always available. Let's look at an example. A farm manager wants to calculate profit and cost ratio for his apple crop. The equations in his model are: EQ EQ EQ EQ EQ EQ
1 : REYEN SOLD x PRICE REVEN 2 : VCOST = SEED + FERT + LABOR MACHO 3 : FCOST MACHD + TAX
4: EXPEN YCOST VCOST + FCOST 5: PROFT = REVEN - EXPEN 6 : RATIO = EXPEN / SOLD
Initially the user wants the output variables calculated, but then he may want to specify an output variable, variable. and have an input variable calculated. For example, example. the farm manager may wish to calculate PROFT, SOLD, PRICE, MACHD, MACHO. and FCOST
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Decision Decisio n Simulation (DSIM) (DS/M)
based on REVEN, EXPEN, RATIO, SEED, FERT, LABOR, LASOR, VCOST, and TAX. ALS will determine the following sequence of calculations ALB will find PROFT, SOLD, PRICE, MACHO, and FCOST without any new instructions from the user.
- solve equation
-
solve solve solve solve - solve
-
equation equation _equat i on equation equation equation
VCOST 2 for VeOST FCOST 4 for FeOST 3 for MACHO 5 for PROFT (; 6
I
for SOLD for PRICE
Another question the farm crop manager might have is: the input variables effecting profit"
"What
ALB ALS would provide the following answer: PROFT == REVEN EXPEN REVEN = SOLD x PRICE EX PEN = VCOST + FeOST FCOST EXPEN VCOST SEED ++ FERT + LABOR LASOR FCOST = MACHO + TAX - PROFIT IS A FUNCTION OF SOLD PRICE SEED FERT
LABOR LASOR
MACHO
TAX
CONCLUSIONS Today's technology can and does measurably support the decision maker. Exploitation of AI in DSS/DSIM OSS/OSIM is largely a matter of implementation. It is academia's role to produce people capable of understanding, building and using what technology provides. It is industry's role to develop and use thQ tha tools responsibly responsibly..
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K.J. Fordyce and G.A. C.A. Sullivan
REFERENCES Bard, V., Y., "APlPIE - An APl Programmed Inference Engine," IBM Cambridge. Scientific Center, Cambridge, MA 02142, 1985. Brown, J., Cook, J., Groner, l., l . , and Eusebi, E., "logic Programming in APl2," IBM Santa Teresa lab, San JosP, Calif. 95150, 1985. Decision Tables: A SYstems Systems Analysis and Documentation Tech.!l..iru!J:. .!l.i...9..!!J: (2nd ed.), IBM, White Plains, NY., NV., 1978. Duchessi, P., "The Conceptual Design of a Knowledge-Based System for Aggregate Planning," School of Business, Suny at Albany, Albany NY 12222, 1985. 1985 . Fox, M. and Smith, S., "ISIS - a Knowledge Based System for Smith,S., Factory Scheduling," Expert Systems. vol. 1, Systems . I, no. I, pp. 25-50, July 1984. Gevarter, W., "Expert Systems: limited But Powerful," lIS ~ Spectrum, pp. 39-45, August 1983. Hagaman, W. et al., "MEDCAT: An APl Program for Medical Diagnosis, Consultation, and Teaching," Proceedings of the 1985 Conference. Kosy, D. and Wise, B. "Self-Explanatory Financial Planning Models," Proceedings of the 1984 National Conference on o~ Artificial Intelligence. Rauch, H. "Probability Concepts for an Expert System Used for Data Fusion." AI ~'a9azine. vol. 5, no. 3, pp. Magazine. 55-60, Fall 1984. Rychener, M., "Expert Systems for Engineering Design," Expert Systems. pp. 30-44, vol. 2, no. I, January 1985. Sullivan, G. and Fordyce, K., "A Decision Support System Paradigm." IBM, H20/906, Poughkeepsie, NV NY 12602, 1983. Sullivan, G. and Fordyce, K., "A Review of Expert Systems," IBM, H20/906, Poughkeepsie, NY 12602,1984. 12602, 1984. Sullivan, G. and Fordyce, K., "AIDA: Artificial Development Aids for APl," Proceedings of the 1985 APl Conference. Wiig, K., K. , "The Arthur D. little Artificial Intelligence Applications Center," Cambridge, MA 02140, 1985.