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Agent Systems
Published in Vivek Kale, Agile Network Businesses, 2017
Patterns are reusable solutions to recurring design problems and provide a vocabulary for communicating these solutions to others. The documentation of a pattern goes beyond documenting a problem and its solution. It also describes the forces or design constraints that give rise to the proposed solution; these are the undocument ed and generally misunderstood features of a design. Constraints can be thought of as pushing or pulling the problem toward different solutions. A good pattern balances these constraints. A set of patterns, where one pattern leads to other patterns that refine or are used by it, is known as a pattern language. A pattern language can be likened to a process: it guides designers who wants to use those patterns through their application in an organic manner. As each pattern of the pattern language is applied, some of the constraints affecting the design will be resolved, while new unresolved constraints will arise as a consequence. The process of using a pattern language in a design is complete when all constraints have been resolved.
From architects’ terms to computable descriptions of spatial qualities
Published in Spatial Cognition & Computation, 2021
Christopher Alexander’s A Pattern Language is a collection of architectural patterns like a dictionary of patterns. A pattern is a description of a design situation that occurs over and over again within a context. Each pattern is a problem-solution set which consists of a problem statement, a verbal description, suggested principles and solution statement, and diagrams. Patterns range from the scale of a city to construction and single objects. For example, the pattern A Place to wait (150) addresses bus stops in the same way as waiting rooms in a surgery. The pattern proposes that there needs to be a mixture of activities – e.g., pool tables, newspaper, and coffee – in a waiting room as well as quiet waiting areas for positive silence. His language, a set of patterns, is the result of over ten years of observation on how people use the built environment. The patterns were created in collaboration with teams of professionals and students; they were tested, surveyed, and verified in a way or the other. Here the purpose, as stated in the second goal of the analysis, is to see if the major terms can be broken down to quantifiable functions.
Using Genetic Algorithm and ELM Neural Networks for Feature Extraction and Classification of Type 2-Diabetes Mellitus
Published in Applied Artificial Intelligence, 2019
Abir Alharbi, Munirah Alghahtani
In the GA feature extraction technique, the choice of features, attributes, or measurements used to represent each pattern presented to the classifier affect the accuracy of the classification function. The attributes used to describe the patterns implicitly define a pattern language. If the language is not expressive enough, it would fail to capture the needed information that is necessary for classification and hence regardless of the learning algorithm used, the accuracy of the classification function learned would be limited by this lack of information (Punch et al. 1993). The feature subset selection problem refers to the task of identifying and selecting a useful subset of attributes to be used to represent patterns from a larger set of often mutually redundant, possibly irrelevant, attributes with different associated measurement costs and/or risks. CAD methods can help in these cases and preform the task of selecting a subset of clinical tests (each with different financial costs, diagnostic values, and risk) to be performed as part of a medical diagnosis task (Alharbi and Tchier 2017; Dalir 2012a, 2012b, 2012c, 2015; Destounis et al. 2004).