Showing items related by title, author, creator and subject.

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  AN EXPERT SYSTEM USING FUZZY SET REPRESENTATIONS FOR RULES AND VALUES TO MAKE MANAGEMENT DECISIONS IN A BUSINESS GAME.

  DICKINSON, DEAN BERKELEY. (The University of Arizona., 1984)

  This dissertation reports on an effort to design, construct, test, and adjust an expert system for making certain business decisions. A widely used approach to recurring judgmental decisions in business and other social organizations is the "rule-based decision system". This arrangement employs staff experts to propose decision choices and selections to a decisionmaker. Such decisions can be very important because of the large resources involved. Rules and values encountered in such systems are often vague and uncertain. Major questions explored by this experimental effort were: (1) could the output of such a decision system be mimicked easily by a mechanism incorporating the rules people say they use, and (2) could the imprecision endemic in such a system be represented by fuzzy set constructs. The task environment chosen for the effort was a computer-based game which required player teams to make a number of interrelated, recurring decisions in a realistic business situation. The primary purpose of this research is to determine the feasibility of using these methods in real decision systems. The expert system which resulted is a relatively complicated, feed-forward network of "simple" inferences, each with no more than one consequent and one or two antecedents. Rules elicited from an expert in the game or from published game instructions become the causal implications in these inferences. Fuzzy relations are used to represent imprecise rules and two distinctly different fuzzy set formats are employed to represent imprecise values. Once imprecision appears from the environment or rules the mechanism propagates it coherently through the inference network to the proposed decision values. The mechanism performs as well as the average human team, even though the strategy is relatively simple and the inferences crude linear approximations. Key aspects of this model, distinct from previous work, include: (1) the use of a mechanism to propose decisions in situations usually considered ill-structured; (2) the use of continuous rather than two-valued variables and functions; (3) the large scale employment of fuzzy set constructs to represent imprecision; and (4) use of feed forward network structure and simple inferences to propose human-like decisions.
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  Establishing a Process Framework for Land Use Planning (invited)

  Lundeen, Lloyd J.; Watershed Systems Development and Application Unit, U. S. Forest Service, Berkeley, California (Arizona-Nevada Academy of Science, 1974-04-20)

  The operational aspects of land use planning, to be effective, must be tied to a well defined planning process. The framework for this process includes a set of main components which are important in solving land use planning problems. These components are linked together in a design related to the basic concepts of decision analysis which has been oriented to natural resource problems. Detailed description can be added to the .process framework to tailor it to a specific problem, study area, or study level. Some of the major components in the framework are an objectives and goals spectrum, problem formulation, physical characterization of the land, social and economic demands analysis, identification of management alternatives and specific activities, simulation of resource response, allocation of resources, visual quality analysis, transportation system analysis, and a data management system. This process framework is d »sinned to he dynamic, user oriented, and compatible with the type of problems encountered in land use planning.
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  An Analysis of Yearly Differences in Snowpack Inventory-Prediction Relationships

  Ffolliott, Peter F.; Thorud, David B.; Enz, Richard W.; Department of Watershed Management, University of Arizona, Tucson 85721; USDA Soil Conservation Service, Phoenix, Arizona 85025 (Arizona-Nevada Academy of Science, 1972-05-06)

  Inventory-prediction relationships between snowpack conditions and forest attributes may be useful in estimating water yields derived from snow, but such relationships are developed usually from source data collected over a short time period. Analyses of long-term data suggest inventory-prediction relationships developed from limited data may have more general application, however. Available records from 18 snow courses in the ponderosa pine type in Arizona provided source data in this study, which was designed to empirically analyze inventory-prediction relationships developed from long-term snow survey records. The primary hypothesis tested and evaluated by statistically analyzing the family of regression equations representing a snow course, was that, given a precipitation input, the distribution of snowpack water equivalent at peak seasonal accumulation is determined by the spatial arrangement of the forest cover, e.g. basal area. Generally 12 of the 18 snow courses evaluated appeared to support the hypothesis, three courses did not, and three courses were considered inconclusive.