Artificial intelligence approaches in model management systems: A survey

('omputer~ rod. I-ngng Vol. 28. No. 2. pp. 291 299. 1995

Pergamon

Copyright q 1995 Elsevier Science t.td Printed in Great Britain All rights reser,,cd (1360-8352.'95 S950 .÷ 0.00

0360-8352(94)00190-I

ARTIFICIAL

INTELLIGENCE

MANAGEMENT

APPROACHES

SYSTEMS:

IN

MODEL

A SURVEY

C H A N G - K Y O SUH, I E U I - H O SUH 2 and D O N G - M A N LEE ~ ~Department of Rehab Medicine, The University of Texas, Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX78284-7798, U.S.A., 2MIS Lab, Department of Industrial Engineering, Pohang Institute of Science and Technology, P.O. Box 125, Pohang 790-600, Korea and ~Department of Business Administration, Kyungpook National University. 1370Sankyeok dong, Buk gu, Teagu 702-701. Korea (Receiced for publication 24 &'ptember 1994)

Abstract.--A model management system (MMS) is one component of a generalized decision support system (DSS) architecture which provides for the creation, storage, manipulation and access of models. Several significant research opportunities exist concerning artificial intelligence application for MMS to enhance its major functions. Thus, here the existing literature is surveyed and categorized based its treatment of model representation and model manipulation functions. With increasing use of DSSs by industrial engineers, it is time for industrial engineers to examine what is currently happening in this important area of information technology.

I. INTRODUCTION Industrial engineers have always been at the forefront of information system development. One tool which they are currently developing is called the decision support system (DSS). This kind of system will eventually allow decision-makers to make decisions based on information drawn from the past, present, and forecasted futures [1]. There is evidence of the increasing use of DSSs by industrial engineers to execute many routine IE functions such as work measurement, time standards and work standards, incentive plans, material handling, and plant layout [2]. DSS is a computer-based information system that supports semi-structured or unstructured decisions. Due to the complexity of these decisions, decision makers need the proper mathematical and/or analytical models to improve their performance [3]. Additionally, for DSS design, a framework consisting of three major components (N.B. data, dialogue, and model) is generally accepted [4, 5]. Finally the use of a model subsystem distinguishes a DSS from traditional transaction-oriented data processing systems. Concerning the use of DSS to improve automated support of decision making in corporate organization, two major areas of research can be identified [6]. The first involves the development of DSS tools and generators that decrease the cost and time necessary to implement and maintain single model DSSs. The second area involves the development of generalized DSS designs that allow: (1) the support of multiple problem-solving using a variety of data sources and models: and (2) the centralized management of organizational models. A model management system (MMS) is one component of a generalized DSS architecture that functions to organize, classify, access and retrieve models from a model base in a manner similar to data base management systems. Early research in MMS considers such models as data or subroutines, and proposes that an MMS should support model creation, storage, retrieval, execution, and maintenance [7]. The objective of this paper is to give an overview of what is currently happening in this important area. After introducing major characteristics of MMS, we briefly outline the potential of artificial intelligence (AI) approaches to MMS. Then, articles are classified based on their model representation scheme and model manipulation functions. 2. MODEL MANAGEMENT SYSTEM The abundant development of DSS tools, DSS generators, and modeling language is resulted in the proliferous use of models to automate the decision making within organizations. However, 291

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this uncontrolled proliferation of the model usage [8], and the continuing need to manipulate and integrate these models in a dynamic manner [9] are two major problems of model management in DSS. To solve this problem, management should recognize models as a valuable corporate resource which require integrated operation, control, and planning like any other resource-- and this must be accomplished by constantly adapting the models to fit new kinds of information and evolving problem-solving strategies [10]. Thus, the major objectives of model management can be summarized as follows [I 1-13]: (I) it aims to facilitate the structuring of decisions so that analytical tools can be used in generating possible solutions; (2) it aims to collect and organize sets of models that are well understood; (3) it aims to derive and create models from the existing models; (4) it aims to manipulate models dynamically as new paradigms are perceived by the user; and (5) it aims to facilitate everyday use; i.e. the basic access of data and models as well as simple recognition of models. Thus, the purpose of an MMS software system is to provide for the creation, storage, manipulation and access of models [14]. Model storage function includes model representation, model abstraction, and physical and logical model storage. Model manipulation functions include model formulation, model utilization, model enhancement, and model output interpretation. In summary, the purpose of MMS is to distance DSS users from the physical aspects of model base storage and processing, just as data management systems serve to distance users from the physical aspects of data base storage and processing [15].

3. OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCEIN MODEL MANAGEMENT SYSTEMS One major DSS principle advanced by researchers assumes that the decision maker could exploit both his own insights and experiences and the powers of the system if he could only interact more directly with the DSS in exploring and structuring the problem situation, applying analytical techniques to generate alternatives, and interpreting and choosing among alternatives [16]. However, present-day DSSs frequently are run by intermediaries, often run in a mechanical problem-solving manner with most of the real problem [17]. Moreover, increased use of end-user computing has resulted in more decision makers wanting to address decision problems directly, rather than using technical experts (i.e. MS/OR analysts) as intermediaries {I 8]. This displacement creates problems since it is the managers, not the MS/OR analysts, who are responsible for making the decision [19]. With intermediary approach, the process of formulating and executing a decision model tends to get quite lengthy and indirect, and the risk of misunderstandings increases [20]. The MMS serves as a bridge linking the decision maker's problem environment with the appropriate models and data residing in the system [21]. Enhancing MMS so it performs tasks that ordinarily require human intelligence in many ways is similar to developing a knowledge-based expert system [16]. In fact, the essence of intelligence may be the ability to model relevant parts of reality in a meaningful way and to draw relevant conclusions (inferences) from such models as a basis for further actions. Therefore, model management involves knowledge about a number of concepts and tasks related to the construction, validation, usage and analysis of models [22]. Eiam e t al. [I 1] categorize such knowledge in 4 ways: technical problem structuring knowledge, application background knowledge, language knowledge, and knowledge about models. Konsynski and Sprague [23] suggest that AI techniques should be used in future research on model management. Accordingly, Liebowitz [24] asserts that operations researchers are starting to contribute their expertise to improving expert systems technology. Similarly, Blanning [15] views intelligence as a property of MMS, and Chang et al. [25] propose a hyperknowledge environment as a unifying paradigm for representing and processing the descriptive knowledge held in a data base and the procedural knowledge held in a model base.

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4. SURVEY OF THE LITERATURE Over the past decade, research on MMS has focused on two major issues: how to represent model knowledge in MMSs (e.g. model representation) and how to provide automated support for various phases of the modeling life cycle (e.g. model manipulation). 4. I. Model representation

Among the various knowledge representation techniques in AI, (I) first-order logic, (2) semantic network, (3) frame and script, and (4) object-oriented paradigm are used commonly with regard to MMS. Logic-based approach. Logical knowledge representation schemes comprise first-order logic (FOL) and production systems. FOL is a formal language defined by its syntax and semantics. The syntax of the language represents facts as well-formed formulas, whereas its semantics are based on truth assignments to well-formed formulas. In the FOL, there are rules of inference, such as modus ponens, universal specialization, and resolution, which make it possible to create new expressions from existing ones. A derived clause is satisfiable if its two given clauses are satisfiable. In a production system, the atomic form of knowledge representation is a rule expressed in the form of IF (antecedent conditions) T H E N (consequences). Forward chaining inference means working forward from the current situation to a conclusion, whereas backward chaining goes the other way, working toward the known facts from an hypothesis. Bonczek et al. [27] present a formal approach to the representation of models in a form compatible with logic-based data representation and resolution-based theorem-proving techniques. Dutta and Basu [28, 29] use domain predicates to represent domain knowledge when it has the usual interpretation of a predicate and model predicates to define the input and output interface of a computational model. Thus, when all predicate input parameters are valid, a model is executed successfully with an appropriate output value. In addition, using FOL, Hee and Lapinski [30] specify the conceptual components of DSS device and give the formal definition of job-shop scheduler model as an example. In a related development, Shaw et al. [31] introduce higher order predicate calculus and production rules to embody a domain-independent problem solving strategy. Semantic network. The term "semantic network" encompasses a family of graph-based representations. Semantic networks are based on some common assumptions: (I) network notations are easy for people to read, (2) they are efficient for computers to process, and (3) they are powerful enough to represent the semantics of natural language. A semantic network represents knowledge as a graph, with nodes corresponding to facts or concepts and their links to relations or associations between concepts. Both nodes and links are generally labeled. Inferences are made by tk)llowing the appropriate links to related concepts. The ROME (Reason-Oriented Modeling Environment) [32] is an experimental DSS generator which has been built to investigate the application of AI techniques to quantitative business analysis. In order to represent the entities in the models it uses a special kind of semantic network which consists of a hierarchy of partitions such that each partition inherits the properties of the nodes above it unless otherwise specified. Elam et al. Ill] propose the Structured Inheritance Network (SI-Net) as the basis for an MMS. An SI-Net is a semantic net for describing concepts and the explicit structuring relationship between these concepts. Through the structure link. one can define I F / T H E N rules. Frame and script representation. The essence of the frame theory is the following [33]: when one encounters a new situation, one selects from memory a structure called a frame which is a data-structure for representing a stereotyped situation. A frame commonly consists of two parts: a name and a list of attribute-value pairs. A script, which can be represented in a frame, has a specialized slot that contains multiple values to express the sequence of actions that are relevant to a situation description. After a particular frame or script has been selected to represent the current context or situation, the primary process in a frame-based reasoning system is often just filling in the details called by its slots. Sometimes the value of slot is directly inherited, i.e. selected by default, or the procedure can be used to make conscious decisions. Another frequently used form of procedural attachment is the addition of routines that are activated when the value of a slot is found or changed.

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Dolk and Konsynski [I 3, 21] have implemented a prototype version of a knowledge-based M MS for LP models called the Generalized eXperimental Math Programming system (GXMP) which uses frame-based knowledge abstractions which consist of data objects, procedures, and assertions all expressed in FOL as the vehicle of knowledge and model representation. Binbasioglu and Jarke [20] use frames to represent business and LP knowledge with their possible attributes within the domain of production planning and resource allocation. The frame system is consequently quite suitable for representing models in a hierarchical organization. In yet another use of frames, AIMM (Agent for Intelligent Model Management) [34] stores information about models and how the models are used. It also stores information about how models can be transformed into other models or different model instances. Object-oriented approach. A fundamental element in an object-oriented approach is the object itself. Objects are entities that combine into a single unit both data and the functions that operate on that data. The object's behavior is entirely determined by its responses to acceptable messages and is independent of the data representation of its instance variables. Therefore, an object is a collection of procedures sharing an unprotected state. Sharing of unprotected data within an object is further combined with strong protection (or encapsulation) against external access. Classes serve as templates from which objects can be created. Inheritance allows us to reuse the behavior of a class in the definition of new classes. Subclasses of a class inherit the operations of their parent class and may add new operations and new instance variables [35, 36]. Geoffrion's structured modeling framework [37] is most widely used to identify the classes of objects which constitute a model. Structured modeling is a unified modeling framework based on acyclic, attributed graphs to represent cross-references between elements of a model, and hierarchies to represent levels of abstraction. On the other hand, Lenard [38] is the first to note that structured modeling has a lot in common with the popular object-oriented programming paradigm. Using the conventions of Smailtalk, she enumerates the classes of objects in structured modeling. Taking this class composition process one step further, Muhanna [39] aggregates classes into higher-level composite classes which correspond to the structured modeling notion of a module. The model itself is then represented by a composite class having attributes, each of whose domain is a composite class representing one of the modules. Huh [40] establishes generic model concepts as a conceptual basis for the framework, based on a system and object-oriented approach, to provide flexible modeling constructs subsuming individual algebraic modeling languages including SML for structured modeling. Objects in MIDAS (Manager's Intelligent Debt Advisory System) [41] fall into three broad groups according to their knowledge types and functional roles within the system. Model objects represent entities in the problem domain such as debts and investments in MIDAS. Objects which store model management knowledge and perform the intermediary functions are referred to as model management objects. System support objects organize the knowledge and procedures necessary for generalized system control and support functions.

4.2. Model manipulation Various model manipulation functions have been good candidates for the AI approach. An intelligent MMS will (I) analyze the problem and construct a model (model formulation), (2) search for appropriate models and/or integrate them instead of reinventing the wheel (model utilization), (3) refine and adjust the model manipulation knowledge (model enhancement), and (4) help the users to interpret the result of models (model output interpretation). Model formulation. Model formulation is a process which aims to construct a decision model from scratch automatically. Model formulation from scratch consists of two steps: (1) identifying the appropriate modeling technique and application domain boundaries, and (2) formulating the model within the chosen modeling/domain combination [20]. Among factors which decide the suitability of the formulation are its representation of reality, acceptability of the formulation by the modeler, availability of data required by the formulation, availability of solver, and the cost of implementation and solution [42]. Bonczek et al. [27] show the process of model formulation by resolution where if all literais having non-operator predicates are eliminated by data retrieval, then further virtual resolution in the guise of ordered module execution is attempted. Then the answer to the user's request is

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discovered when a null resolvent is virtually attained. The ACS (Actuarial Consulting System) [9] composes models in life insurance mathematics from a library of stored formulas using AI technique. Binbasioglu and Jarke [20] present a domain-specific approach to knowledge-based model formulation. It combines the use of syntactic knowledge about LP with semantic guidance of specific application domain. Bhargava and Krishnan [42] separate the use of domain knowledge from the use of general model formulation knowledge. Modeling knowledge is employed in the model formulation process to obtain a mathematical representation from the qualitative model, while domain knowledge is employed to obtain this qualitative model from an initial problem specification. The AIM M [34] uses model-specific and meta-knowledge to formulate specific models from source (data) to sink (goal) by matching and chaining primitive modules from the central module library. Model utilization. To avoid reinventing the wheel is the objective of model utilization. After receiving the user's problem description, it involves two types of activities: (I) selecting and applying the most appropriate decision model from a model base, and (2) constructing a complicated model by integrating existing models in the model base. The capability of model selection helps the user determine what models are available to produce the requested information and then automatically selects or allows the user to choose a model for execution [7]. The key step in the model integration process is the development of a master plan for building a composite model when there is no single model available for producing the desired information [43]. There are two dimensions which must be considered in the process of model integration: definitional (model representation) and procedural (model manipulation). Definitional integration involves the logical linking of similar model representations whereas procedural integration concerns the linking of processes to form operators which subsequently manipulate these integrated representations [44]. Dutta and Basu [28] deal with the development of a method for the automatic selection, synthesis, and sequencing of models for computational models required to generate query responses. Fedorowicz and Williams [45] describe a two-level knowledge scheme: the general (or macro) level and the instantiated (or micro) level. The G U T S (Graph Upkeep and Task Support system) is a macro level process controller which accepts a clause form query and instantiates frames associated with models in the model base. Liang [7] depicts a set of data as a node. a set of functions as an edge, and a basic model as a combination of two nodes and one connecting edge. Based on this scheme, the graph-based mechanisms for model integration and selection are discussed. Banerjee and Basu [18] present a framework to support model type selection in which the selection process is based on matching problem parameters with model parameters. Dempster and Ireland [41] describe the model integration aspects of MIDAS which provides assistance to non-expert users in the configuration and complementary use of multiple model types in financial management domain. Muhanna [39] notes four levels of model integration: specific models, model classes, modeling paradigms, and discipline-specific modeling traditions. He also presents the abstraction under which individual models can be coupled to assemble larger models by interconnecting the output ports of one model with the input ports of others. Model enhancement. Better model enhancement support can be provided when the MMS acquires new knowledge about a problem domain from its interaction with the user and captures it so that it can be accessed at a later time [I 1]. The interaction with the end user can also lead to incremental enhancement of the knowledge base through limited machine learning features [20]. The major applications of machine learning to MMS have four aspects [31]: (I) (2) (3) (4)

the the the the

acquisition of model manipulation knowledge, refinement of model manipulation knowledge, refinement of model representation, and creation of a model selection heuristic.

Using learning-by-analogy, AIMM [34] learns from the modeling task. AIMM acquires new facts by transforming and augmenting existing primitive modules, meta-models, and meta-knowledge all of which bear a strong similarity to a desired new concept or solution, into a form effectively useful in the new situation. Similarly, Shaw et al. [31] have constructed a general framework with four learning components: the Instance Selector, the Problem Solver, the Critic, and the Learning

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Module, which aims to generate model manipulation knowledge, refine the acquired knowledge and model representation, and adjust the utility function used to select the best solution. Versioning and version history management are other critical factors for an effective MMS because model development processes are iterative and tentative. Thus, Muhanna [39] implements SYMMS (SYstem-oriented Model Management System) which manages model versions and revisions histories as a step-wise refinement based on an object-oriented paradigm, where a model version is a class which inherits all the properties of its corresponding model type, keeping the interface properties unchanged while adding specification for the behavior. M o d e l output interpretation. Another primary role of AI-based MMS is to aid in model analysis and interpretation. Even if MMS provides extremely powerful functions, it might still not be used if the users have any trouble understanding the result. Consequently, the ROME [32] performs the task of explaining financial modeling results, and explanations are initiated either as a result of deviations from user-entered expectations, or in response to specific user requests. Another consideration in model output interpretation is the understanding that natural language is widely used among scholars. For example, Greenberg [46] presents a natural language discourse model to explain a linear programming model and its computed solution. This discourse model is implemented in ANALYZE. Teng et al. [47] also emphasize natural language processor to make user-friendly interpretations. Finally, before closing this section, we would like to maintain one additional consideration about natural language. Natural language may appear to be an ideal interface model to researchers outside of human factors. However, work within the field of human factors indicates that it may not be the best interface mode because (I) it is too inexact, (2) does not apply the appropriate constraints, and (3) can be misleading when compared to more specialized interface languages [48]. Table I presents a rough categorization of the literature. This classification remains tentative, especially due to possibility of different interpretations concerning available documentation. Still, it is a valuable guide for understanding the current development of the AI approaches in MMS research.

5. CONCLUSIONS Any survey can be by no means exhaustive. Its purpose is to give an overview of various AI approaches within the context of MMS, rather than a comprehensive description of MMS. An intelligent MMS is a system which, when given a decision problem, facilitates the process by solving more structured parts without significant effort on the part of decision makers and guiding decision makers by providing them with much relevant information [18]. Although a series of surveys [15, 19, 49] indicate that MMS has matured as a research area, there is much yet to do before MMS can provide automated, intelligent, reliable, user-friendly decision support. First, although model representation is essential for developing intelligent MMS, the nature and purpose of model representation techniques with regard to MMS has not been sufficiently investigated. Table 2 summarizes the pros and cons of proposed model representations. Applegate et al. [14] present the system design objectives and the complexity of the system domain as a basis for the selection of a model representation of an MMS. Other system characteristics, such as usefulness, efficiency, maintenance of an evolutionary knowledge base, and support of an interactive consultant, are all affected by the choice of a particular representation scheme [26]. Ot" course, no one model representation scheme will outdo all the others. Thus, we need more rigorous research in this area. Second, it is the right time to move into a full-fledged MMS. A number of solutions on each model manipulation function have led to a growing discipline of model management. However, current MMSs proposed do not treat the whole gamut of model manipulation, but remain in the island-of-progress. The lack of this feature limits the usefulness of these systems. In other words, MMS should support the entire process of decision making. Third, research should focus on real implementation of intelligent MMS. Despite a number of favorable evidences, intelligent MMS revolution has not yet happened. There is still widespread

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r e s e r v a t i o n a b o u t t h e p r e d i c t i o n t h a t d e c i s i o n m a k i n g s in t h e o r g a n i z a t i o n w o u l d b e i m p r o v e d enormously. We need a number of success stories concerning various MMS application areas. In s u m m a r y , t h e u l t i m a t e g o a l is t o m a k e t h e M M S m o r e i n t e l l i g e n t t h r o u g h v a r i o u s A I techniques by developing better knowledge representation approaches, by providing better user interfaces, by supporting parallel model solving, and by incorporating machine learning for model e n h a n c e m e n t . T h e n t h e m a n a g e r c a n use d i r e c t l y , i n s t e a d o f g o i n g t h r o u g h a n i n t e r m e d i a r y , a u t o m a t i c m o d e l - b u i l d i n g t o o l s in d e c i s i o n m a k i n g .

Acknowledgements--The authors wish to thank Brock Brady, and the anonymous referees for their helpful comments and suggestions on an earlier draft of this paper.

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