Explainable Decision-Making for Water Quality Protection

All professional decisions prepared for a specific stakeholder can and must be explained. The primary role of explanation is to defend and reinforce the proposed decision, supporting stakeholder confidence in the validity of the decision. In this paper we present the methodology for explaining results of the evaluation of alternatives for water quality protection for a real-life project, the Upper Neuse Clean Water Initiative in North Carolina. The evaluation and comparison of alternatives is based on the Logic Scoring of Preference (LSP) method. We identify three explainability problems: (1) the explanation of LSP criterion properties, (2) the explanation of evaluation results for each alternative, and (3) the explanation of the comparison and ranking of alternatives. To solve these problems, we introduce a set of explainability indicators that characterize properties that are necessary for verbal explanations that humans can understand. In addition, we use this project to show the methodology for automatic generation of explainability reports. We recommend the use of explainability reports as standard supplements for evaluation reports containing the results of evaluation projects based on the LSP method

JOURNAL OF AUTOMATIC CONTROL, UNIVERSITY OF BELGRADE; VOL. 13(2):23-33, 2003 © Optimizing Computer System Configurations

Abstract- We present a quantitative method for selecting optimum configurations of computer systems. For each configuration we specify a set of requirements reflecting user’s needs. The level of satisfaction of requirements is called the global preference score. Using this indicator and the total cost of each configuration we solve the following optimization problems: (1) select a configuration which maximizes the global preference score for a constrained cost of computer system, (2) select a configuration which attains a given level of global preference for a minimum cost, and (3) select a configuration which attains the maximum global preference/cost ratio. We first present the optimization method and then a complete case study of computer optimization. 1

Idempotent weighted aggregation based on binary aggregation trees

We propose weighted aggregation algorithms for creating general idempotent weighted aggregators of n variables derived from related symmetric idempotent aggregators of two variables. This computational method, together with interpolative aggregation, can be used for the development of general idempotent logic aggregators that satisfy a variety of conditions necessary for building decision models in the area of weighted compensative logic

UDC 004.738.52 Evaluation and Comparison of Search Engines Using the LSP Method

Abstract. We present a comprehensive model for quantitative evaluation and comparison of search engines. The model is based on the LSP method for system evaluation. The basic contribution of our approach is the aggregation of all relevant attributes that reflect functionality, usability, and performance of search engines. In this respect our model is fully consistent with the ISO 9126 standard for software product evaluation. Performance analysis of competitive search engines is based on our search engine benchmarking tool (SEben) that is also described in the paper. 1

Extension of bivariate means to weighted means of several arguments by using binary trees

ABSTRACTAveraging aggregation functions are valuable in building decision making and fuzzy logic systems and in handling uncertainty. Some interesting classes of averages are bivariate and not easily extended to the multivariate case. We propose a generic method for extending bivariate symmetric means to n-variate weighted means by recursively applying the specified bivariate mean in a binary tree construction. We prove that the resulting extension inherits many desirable properties of the base mean and design an efficient numerical algorithm by pruning the binary tree. We show that the proposed method is numerically competitive to the explicit analytical formulas and hence can be used in various computational intelligence systems which rely on aggregation functions

Explainable Decision-Making for Water Quality Protection

All professional decisions prepared for a specific stakeholder can and must be explained. The primary role of explanation is to defend and reinforce the proposed decision, supporting stakeholder confidence in the validity of the decision. In this paper we present the methodology for explaining results of the evaluation of alternatives for water quality protection for a real-life project, the Upper Neuse Clean Water Initiative in North Carolina. The evaluation and comparison of alternatives is based on the Logic Scoring of Preference (LSP) method. We identify three explainability problems: (1) the explanation of LSP criterion properties, (2) the explanation of evaluation results for each alternative, and (3) the explanation of the comparison and ranking of alternatives. To solve these problems, we introduce a set of explainability indicators that characterize properties that are necessary for verbal explanations that humans can understand. In addition, we use this project to show the methodology for automatic generation of explainability reports. We recommend the use of explainability reports as standard supplements for evaluation reports containing the results of evaluation projects based on the LSP method

Logic operators and sibling aggregators for Z-grades

Trust in data is a crucial aspect of criterion-based flexible query answering and decision making. Inspired by Zadeh’s concept Z-number, we introduce the concept of a Z-grade and focus on some elementary aspects of aggregating Z-grades. A Z-grade, z, has two components, z=(s,c). The first component, s, is a satisfaction grade that can for example be used to express to what extent a given data element satisfies a given criterion. The second component, c, is a confidence grade that expresses how confident we can be about s. For example, in case we have less trust in the data element, this could result in a lower confidence in the outcome s of the criterion evaluation. Logical processing and aggregation of satisfaction grades are important aspects of criterion handling. When applied to Z-grades, the computation of the resulting confidence grade depends on the computation of the resulting satisfaction grade. For that purpose, novel logic operators and so-called sibling aggregators for Z-grades are proposed and studied in the paper