A Model-Based Approach to Investigate Performance Improvements in Rule-Based Expert Systems

One of the major issues in the development of large, rule-based expert systems is related to improving their performance efficiency. One way to address this issue is by reducing the number of unsuccessful tries a system goes through before executing a rule to establish a goal or an intermediary fact. On the average, the number of unsuccessful tries can be reduced if the rules that are tried first are those that are expected to execute most frequently, and this can be established by extracting information on the probability distributions of the input parameters. In this paper, a rule base is modeled as a network and simulated to investigate potential performance improvements by changing the order used to test the rules. The model of the rule base is also used to investigate performance gains achieved by parameter factorization and premise clause reordering.     

The Determinants of DSS Success: An Integrated Model*

The results of a field study investigating the determinants of decision support systems (DSS) success are presented. A multivariate model was developed and tested using multiple regression hierarchical analysis on responses from 118 DSS users. Several specific hypotheses are also proposed and tested. Data analyses indicate that DSS success, as measured by DSS satisfaction and perceived benefits, depends on several factors: previous user experience with DSS, user involvement, user training, top management support, information sources, the level of managerial activity, and task structure, difficulty and interdependence. Based on the research results, several suggestions are made to improve the likelihood of successful DSS implementation.     

An Application of the AXIS Solution Framework to Multiple Objective Aggregate Production Planning

This paper provides data on the first application of a prototype of the AXIS solution framework. AXIS (algorithms combined with knowledge systems in an interactive sequence) is a framework for interactively combining structured algorithms that seek a best solution with knowledge-based expert systems that seek expert heuristic solutions. This paper tests the framework using an interactive multiple objective integer programming algorithm combined with heuristics taken from the domain of aggregate production planning. The results indicate the AXIS framework can be successful in generating high quality solutions, in vastly reduced solution times compared to the structured algorithms, at much lower costs compared to the expert heuristics working alone.     

A Prototype Expert Decision Support System for the Market Appraisal of the Single Family Residence

The assessment of the current market value of residential property is a potential source of anxiety for the typical homeowner. Common criticisms of the existing appraisal process include excessive subjectivity and inconsistency across properties and through time. This paper examines the nature of decision support required for valuation decisions and the appropriateness of applying expert system technologies to evaluate the recoverable value of the single-family residence. The expert system approach allows for the integration of the qualitative and quantitative aspects of appraisal, while introducing the beneficial dimensions of increased objectivity, comprehensiveness, and consistency. A prototype expert system is offered that enables the end user to conduct a market analysis of a subject property. Unlike most expert system applications that seek a diagnostic or classification response, this study explores a numerical intent for the system, in a decision-making environment that is traditionally viewed as highly judgmental. The system's early validation results show promise of proving effective as such an evaluation aid.     

The Effects of Memory Structure on Using Rule-Based Expert Systems for Training: A Framework and an Empirical Test*

One justification for eliciting and representing the judgment strategy of an expert in a rule-based expert system (RBES) is to facilitate knowledge transfer to individuals with less expertise. However, prior research suggests complexities and potential problems when using RBESs for training. In this paper, a conceptual framework of user learning from RBESs is presented. It is proposed that learning may be ineffective when the problem representation of the RBES is inconsistent with the user's mental representation of the task-domain knowledge. An experiment was conducted to examine the effects of consistency (inconsistency) between the problem-solving strategy of RBESs and individuals' memory structures. Groups of subjects whose memory structure either matched or did not match two RBESs' problem-solving strategies were examined using an internal control evaluation task. The results suggest that learning was facilitated only for groups with congruence between the RBES's problem-solving strategy and a subject's memory structure.     

A Service Quality Process Map for Credit Card Processing

Evaluating marketing (external) and operations (internal) service quality performance in most organizations is a data-oriented management task that involves many variables for many different types of services. These two diverse sets of service quality information seldom “match up well” because the marketing and operations functions define and evaluate service quality “their way.” Management's interpretation of what the data “mean” often contributes to extra meetings, unnecessary disagreements within and between functional areas, and poor decision making. This situation can affect the quality and timing of decisions, and ultimately, the organization's profitability and market share. This paper quantitatively relates the customer's evaluation (perception) of service quality directly to the activities and performance criteria of the service delivery process for a credit card processing center. Basic statistical analyses and a recursive path analysis model define the relationships between marketing- and operations-based service quality information. The idea of a “service quality process map” is introduced. A service quality process map relates a flowchart of the service delivery process to the customer's perception of service using the power of multivariate data analysis. Management insights gained from an analysis of marketing (external) and operations (internal) measures of service quality performance can be used to allocate resources wisely. The objective is to gain the most improvement in the customer's perception of service for the least cost. Service quality with this type of evaluation capability can be used to gain competitive advantage in the marketplace.     

SCAT AND SPAT: LARGE-SCALE COMPUTER-BASED OPTIMIZATION SYSTEMS FOR THE PERSONNEL ASSIGNMENT PROBLEM

The personnel assignment problem involves the assignment of people to tasks, projects, etc., in a manner that maximizes a measure of aggregate utility for the assignees subject to several assignment constraints. In addition to computationally efficient solution algorithms, the effective implementation of mathematical models to solve the personnel assignment problem must include data processing systems with comprehensive error checking/resolution procedures, end user reports, documentation, and training. This paper summarizes two successful computer-based systems for solving large-scale personnel assignment problems that frequently occur in university settings. SCAT, the Student-Company Assignment Technique, assigns students to job interview slots. SPAT, the Student-Project Assignment Technique, assigns students to project teams in an MBA field project course. The SCAT and SPAT approaches can be generalized to a wide variety of personnel assignment problems in university, industry, government, and military settings.     

EFFECTS OF CONFLICTING ANALYSES ON MANAGERIAL DECISION MAKING: A LABORATORY EXPERIMENT

Researchers in a variety of disciplines have recommended the use of multiple conflicting data analyses to improve managerial decision making through the challenging of assumptions. This study deals with the effects of single data analyses and conflicting analyses on managers' solutions to a case analysis task. Results showed that managers who received conflicting analyses produced solutions with higher expected profits than those who received single analyses. Implications for the use of analytic decision aids are noted.     

AN ADAPTIVE DECISION SUPPORT SYSTEM FOR ACADEMIC RESOURCE PLANNING

Institutions of higher learning are growing increasingly interested in the use of model-based approaches to their resource allocation problems. Recent modeling approaches, however, have failed to consider that resource allocation planning is not a well-structured decision process. Additionally, many decision makers are necessarily involved in the academic planning process and may assume dissimilar perspectives on the importance of achieving different goals and objectives. Furthermore, satisfactory allocation solutions can be expected to vary considerably from decision maker to decision maker as the individual's cognitive processes, perceptions, and evaluations are taken into consideration. This paper describes a decision support system (DSS) approach that attempts to adapt to a variety of academic decision makers with differing planning views in an environment of multiple conflicting objectives. This DSS, which was successfully tested on four academic decision makers in a large midwestern university, shows considerable promise for providing decision support to decision makers with varied problem-solving styles.