The main purpose of the present book is to develop a general framework for population-based metaheuristics based on some basic concepts of set theory. The idea of the framework is to divide the population of individua...
ISBN:
(数字)9783031134296
ISBN:
(纸本)9783031134289
The main purpose of the present book is to develop a general framework for population-based metaheuristics based on some basic concepts of set theory. The idea of the framework is to divide the population of individuals into subpopulations of identical sizes. Therefore, in each iteration of the search process, different subpopulations explore the search space independently but simultaneously. The framework aims to provide a suitable balance between exploration and exploitation during the search process. A few chapters containing algorithm-specific modifications of some state-of-the-art metaheuristics are also included to further enrich the book.
These explicitly human-centric issues in the development, design, analysis, and implementation of the HCI systems are discussed in the book. A comprehensive state of the art is given complemented with original own pro...
ISBN:
(数字)9783030900045
ISBN:
(纸本)9783030900038
These explicitly human-centric issues in the development, design, analysis, and implementation of the HCI systems are discussed in the book. A comprehensive state of the art is given complemented with original own proposals. As opposed to more traditional formal and IT based analyses, the discussion is here more focused on relevant research results from psychology and psychophysiology, and other soft, cognitive, etc., sciences. Remarks on the relevance of affective computing are also mentioned.
Modern AI techniques –- especially deep learning –- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these rec...
ISBN:
(数字)9783031099748
ISBN:
(纸本)9783031099731
Modern AI techniques –- especially deep learning –- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications.
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