Swarm Intelligent Systems

This volume offers a wide spectrum of sample works developed in leading research throughout the world about innovative methodologies of swarm intelligence and foundations of engineering swarm intelligent systems as well as applications and interesting experiences using the particle swarm optimisation.Swarm intelligence is an innovative computational way to solve hard problems. In particular, particle swarm optimization, also commonly known as PSO, mimics the behavior of a swarm of insects or a school of fish. If one of the particle discovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in multidimensional space that have two characteristics: a position and a velocity. These particles wander around the hyperspace and remember the best position that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions. Vagueness Is Central To The Flexibility And Robustness Of Natural Language Descriptions. Vague Concepts Are Robust To The Imprecision Of Our Perceptions, While Still Allowing Us To Convey Useful, And Sometimes Vital, Information. The Study Of Vagueness In Artificial Intelligence (ai) Is Therefore Computer Systems. Such A Goal, However, Requires A Formal Model Of Vague Concepts That Will Allow Us To Quantify And Manipulate The Uncertainty Resulting From Their Use As A Means Of Passing Information Between Autonomous Agents. This Volume Outlines A Formal Representation Framework For Modelling And Reasoning With Vague Concepts In Artificial Intelligence. The New Calculus Has Many Applications, Especially In Automated Reasoning, Learning, Data Analysis And Information Fusion. This Book Gives A Rigorous Introduction To Label Semantics Theory, Illustrated With Many Examples, And Suggests Clear Operational Interpretations Of The Proposed Measures. It Also Provides A Detailed Description Of How The Theory Can Be Applied In Data Analysis And Information Fusion Based On A Range Of Benchmark Problems. -- From Back Cover. Introduction -- Vauge Concepts And Fuzzy Sets -- Label Semantics -- Multi-dimensional Multi-instance Label Semantics -- Information From Vague Concepts -- Learning Linguistic Models From Data -- Fusing Knowledge Data -- Non-additive Appropriateness Measures. Jonathan Lawry. Includes Bibliographical References (p. [235]-243) And Index. Vague concepts are intrinsic to human communication. Somehow it would seems that vagueness is central to the flexibility and robustness of natural l- guage descriptions. If we were to insist on precise concept definitions then we would be able to assert very little with any degree of confidence. In many cases our perceptions simply do not provide sufficient information to allow us to verify that a set of formal conditions are met. Our decision to describe an individual as'tall'is not generally based on any kind of accurate measurement of their height. Indeed it is part of the power of human concepts that they do not require us to make such fine judgements. They are robust to the imprecision of our perceptions, while still allowing us to convey useful, and sometimes vital, information. The study of vagueness in Artificial Intelligence (AI) is therefore motivated by the desire to incorporate this robustness and flexibility into int- ligent computer systems. This goal, however, requires a formal model of vague concepts that will allow us to quantify and manipulate the uncertainty resulting from their use as a means of passing information between autonomous agents. I first became interested in these issues while working with Jim Baldwin to develop a theory of the probability of fuzzy events based on mass assi- ments. Vagueness is central to the flexibility and robustness of natural language descriptions. Vague concepts are robust to the imprecision of our perceptions, while still allowing us to convey useful, and sometimes vital, information. The study of vagueness in Artificial Intelligence (AI) is therefore motivated by the desire to incorporate this robustness and flexibility into intelligent computer systems. Such a goal, however, requires a formal model of vague concepts that will allow us to quantify and manipulate the uncertainty resulting from their use as a means of passing information between autonomous agents. This volume outlines a formal representation framework for modelling and reasoning with vague concepts in Artificial Intelligence. The new calculus has many applications, especially in automated reasoning, learning, data analysis and information fusion. This book gives a rigorous introduction to label semantics theory, illustrated with many examples, and suggests clear operational interpretations of the proposed measures. It also provides a detailed description of how the theory can be applied in data analysis and information fusion based on a range of benchmark problems This volume introduces a formal representation framework for modelling and reasoning, that allows us to quantify the uncertainty inherent in the use of vague descriptions to convey information between intelligent agents. This can then be applied across a range of applications areas in automated reasoning and learning. The utility of the framework is demonstrated by applying it to problems in data analysis where the aim is to infer effective and informative models expressed as logical rules and relations involving vague concept descriptions. The author also introduces a number of learning algorithms within the framework that can be used for both classification and prediction (regression) problems. It is shown how models of this kind can be fused with qualitative background knowledge such as that provided by domain experts. The proposed algorithms will be compared with existing learning methods on a range of benchmark databases such as those from the UCI repository. "This volume outlines a formal representation framework for modelling and reasoning with vague concepts in Artificial Intelligence. The new calculus has many applications, especially in automated reasoning, learning, data analysis and information fusion. This book gives a rigorous introduction to label semantics theory, illustrated with many examples, and suggests clear operational interpretations of the proposed measures. It also provides a detailed description of how the theory can be applied in data analysis and information fusion based on a range of benchmark problems."--Résumé de l'éditeur.