Verification and validation of knowledge-based systems with an example from site selection.

In this paper, the verification and validation of Knowledge-Based Systems (KBS) using decision tables (DTs) is one of the central issues. It is illustrated using real-market data taken from industrial site selection problems.One of the main problems of KBS is that often there remain a lot of anomalies after the knowledge has been elicited. As a consequence, the quality of the KBS will degrade. This evaluation consists mainly of two parts: verification and validation (V&V). To make a distinction between verification and validation, the following phrase is regularly used: Verification deals with 'building the system right', while validation involves 'building the right system'. In the context of DTs, it has been claimed from the early years of DT research onwards that DTs are very suited for V&V purposes. Therefore, it will be explained how V&V of the modelled knowledge can be performed. In this respect, use is made of stated response modelling designs techniques to select decision rules from a DT. Our approach is illustrated using a case-study dealing with the locational problem of a (petro)chemical company in a port environment. The KBS developed has been named Matisse, which is an acronym of Matching Algorithm, a Technique for Industrial Site Selection and Evaluation.Selection; Systems;

Aproximación bibliográfica a los Sistemas de Información Geográfica aplicados a la Ordenación del Territorio y los Recursos Naturales.

Sin resume

A decision support system for planning and management of sustainable livestock production in the Midwest

Managing livestock operations in an environmentally sound way presents a major challenge to the livestock production industry. Excessive amounts of manure can cause major environmental problems if not properly managed. The contamination of surface and subsurface water supplies due to non-point source pollution from livestock production has increased public concern in regards to large livestock operations. Another source of pollution from livestock facilities is atmospheric pollution in the form of odor. Odorous gaseous, such as carbon dioxide, methane, ammonia, and other gaseous from livestock operations are a health hazard and nuisance to neighboring populations. Analysis of the environmental impacts of livestock production has depended on the use of emerging geospatial information systems as well as biophysical models to predict agricultural non-point source pollution.;The overall goal of this research was to develop a DSS to facilitate analysis and management of environmental problems associated with livestock production. To accomplish this objective, a GIS-based decision support system (DSS) was developed that integrates a multi-criteria site selection model, a biophysical model, and an atmospheric dispersion model into a framework that can assist planners and decision-makers in selecting suitable land areas both for siting livestock operations and for manure application, and to analyze the potential water quality and regional atmospheric consequences of production practices. In this study, LPRDSS was used to assess areas in Taylor County, Iowa for siting large-scale swine confinement operations and to evaluate the impacts on water quality in the Hundred and Two Mile River watershed. The DSS was also used to assess potential regional air quality problems associated with those sites

Actas del III Seminario Hispano-Marroquí (Algeciras, abril de 2011)

Actas del III Seminario Hispano-Marroquí (Algeciras, abril de 2011

Design of a View-Based DSS for Location Planning

This paper describes the design of a DSS for locating facility networks. The proposed DSS is based on the principle of dynamic data definitions. The declarative and procedural forms of knowledge involved are identified by a logical analysis of planning tasks. The DSS supports an iterating process of adjusting and evaluating plan options. A flexible and interactive problem solving environment is achieved by means of a user defined set of views that captures both forms of knowledge. Each view describes the system to be planned in terms of a set of variables and attached evaluation procedures. The views are dynamic and linked data structures, so that changes in one view automatically lead to updating all linked views. The DSS supports both the specification of the set of views and its application to solve a specific location problem

Design of a view-based DSS for location planning

The traditional approach in DSS falls short of providing a highly interactive problem solving environment for planning. Often, cumbersome procedures are required to implement optional plans and obtain feedback information. In dynamic graphic systems, the user is able to view different linked graphic representations (e.g., spatial or statistical graphs) of statistical data and interact (e.g., selecting items) with these graphics. In this paper we describe the design of a DSS for planning facility locations, which uses principles of dynamic graphics to achieve a highly interactive problem solving environment. As in dynamic graphic systems, the user interacts with the DSS through active and linked views. However, where views in dynamic graphics are different representations of a given dataset, the views in the DSS are active data structures describing the facility system to be planned from different perspectives. The declarative and procedural forms of knowledge involved are identified by a logical analysis of planning problems. A frame-based formalism is proposed to represent the knowledge contained in the views. The main advantage of this view-based approach is that it offers the user a highly flexible and interactive environment for performing ‘what-if’ analyse