A Computational Model of Initial Representation Formation for DSS

Initial Representations of a problem can be used in the problem structuring process to create a model of the problem at hand. We propose a framework for a computational model of initial representation formation that can be used in the context of a decision support system. Such a model can be used to assist in the intelligence phase of decision making by helping structure the problem and can eventually be extended to provide support to the learning function in a DSS that uses intelligent agents. Introduction Certain classes of decision-making situations encountered in business involve solving ill-structured problems. Such problems are characterized by either the lack of a clearly defined goal or a solution that is clear and exact. The problem solver has to create the problem structure by defining the problem – specifying the goals and specifying what determines the end of the problem-solving process. Decision Support Systems (DSS) are frequently used to aid managers in making decisions when facing ill-structured problems. However, DSS are typically not able to assist the decision-maker in the process of defining the problem or structuring the problem; they typically can assist the decision-maker only when most of the characteristics and information about the problem are known. Creating Initial Representations (Simon, 1973) are one way of structuring an ill-structured problem. The initial representation is the problem solver's first impression, or initial mental model, of the problem. The initial representation imposes boundaries on the reasoning process by guiding the problem solver during the search for information and in the application of solution methods. This problem representation continues to be modified during the solution process till the goal is reached. In this paper, we propose a computational model of the initial representation process. The program will serve to implement a process model of the initial representation process (Abraham, 1997) and also to provide a facility to test the model using actual business data. Motivation The motivation for studying this process stems from criticism of research in the area of Decision Support Systems. Stohr and Konsynski (1992) point out that researchers in this area have neglected to study the process of problem structuring which is a very important phase of problem solving and hence, the decision making process. Our prior research (Abraham, 1997) addressed this criticism with the aim of gaining a better understanding of the problem structuring process. The proposed research will take it one step further by developing a computational model of the process. When this model has eventually been tested fully, it may be used to enhance systems that support decision making. Developing a computational model is an accepted method of demonstrating the functionality of the model and thereby validating it (Buchanan, 1988; Simon, 1969). This step is seen as a necessary phase in significant AI projects because it affords the ability to test complex cognitive or process models in a practical manner. Process Model of Initial Representation Formation There are two basic components needed in the computational model, the actual process of initial representation and a structure that stores the initial representation of the problem. The structure can be represented using a object with four basic slots. The example shown here uses the domain of LAN planning to illustrate the content of the slots. The structure is used is by the Initial Representation Processor, described below, to store information about the problem during the initial stages of problem solving. When the problem structuring process is completed, the initial representation structure serves as a collection of information about the problem during the solution process. The Initial Representation Processor, shown below, is an iterative process that seeks to find information to fill the initial representation structure. It parses the information in the problem space and assigns it to