scholarly journals Computational Intelligence for Mobile Robotic Systems - Decision Making, Learning, and Planning -

2000 ◽  
Vol 12 (3) ◽  
pp. 194-201 ◽  
Author(s):  
Toshio Fukuda ◽  
◽  
Naoyuki Kubota ◽  
Keyword(s):  
Decision Making ◽  
Computational Intelligence ◽  
System Architecture ◽  
Evolutionary Computing ◽  
Robotic Systems ◽  
Neural Computing ◽  
Changing Environments ◽  
Fuzzy Computing

This paper describes recent topics of computational intelligence. The intelligent capabilities will be required for the various systems to adapt to dynamically changing environments. First, we introduce the computational intelligence including evolutionary computing, neural computing, and fuzzy computing. Next, some of the important problems including the system architecture, structured intelligence, emerging system, and implementation methods are discussed for mobile robotic systems from the viewpoint of coevolution.

scholarly journals Computational intelligence in product design engineering: review and trends

2021 ◽  
Author(s):  
Filippo A. Salustri
Keyword(s):  
Decision Making ◽  
Product Design ◽  
Engineering Design ◽  
Computational Intelligence ◽  
Design Automation ◽  
State Of The Art ◽  
Design Engineering ◽  
Broad Perspective ◽  
Research Issues ◽  
Product Engineering

Product design engineering is undergoing a transformation from informal and largely experience-based discipline to a science-based domain. Computational intelligence offers models and algorithms that can contribute greatly to design formalization and automation. This paper surveys computational intelligence concepts and approaches applicable to product design engineering. Taxonomy of the surveyed literature is presented according to the generally recognized areas in both product design engineering and computational intelligence. Some research issues that arise from the broad perspective presented in the paper have been signaled but not fully pursued. No survey of such a broad field can be complete, however, the material presented in the paper is a summary of state-of-the-art computational intelligence concepts and approaches in product design engineering. Keywords: Computational intelligence, engineering design, product engineering, decision making, design automation

Introduction

2013 ◽  
pp. 1-5
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Intelligence ◽  
Evolutionary Computing ◽  
Historical Background ◽  
Intelligent Computing ◽  
Biometric Security ◽  
Introductory Overview ◽  
Over Time

This chapter presents an introductory overview of the application of computational intelligence in biometrics. Starting with the historical background on artificial intelligence, the chapter proceeds to the evolutionary computing and neural networks. Evolutionary computing is an ability of a computer system to learn and evolve over time in a manner similar to humans. The chapter discusses swarm intelligence, which is an example of evolutionary computing, as well as chaotic neural network, which is another aspect of intelligent computing. At the end, special concentration is given to a particular application of computational intelligence—biometric security.

Cat Swarm Optimization Supported Data Mining

2010 ◽  
pp. 26-43
Author(s):  
Pei-Wei Tsai ◽  
Jeng-Shyang Pan ◽  
Bin-Yih Liao ◽  
Shu-Chuan Chu ◽  
Mei-Chiao Lai
Keyword(s):  
Data Mining ◽  
Computational Intelligence ◽  
Evolutionary Computing ◽  
Swarm Optimization ◽  
Cat Swarm Optimization

This chapter reviews the basic idea and processes in data mining and some algorithms within the field of evolutionary computing. The authors focus on introducing the algorithms of computational intelligence since they are useful tools for solving problems of optimization, data mining, and many kinds of industrial issues. A feasible model of combining computational intelligence with data mining is presented at the end of the chapter with the conclusions.

A Tool for Assisting Group Decision-Making for Consensus Outcomes in Organizations

2009 ◽  
pp. 941-963
Author(s):  
Faezeh Afshar ◽  
John Yearwood ◽  
Andrew Stranieri
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Computational Intelligence ◽  
Group Decision ◽  
Shared Understanding ◽  
Deliberative Process ◽  
Computer Mediated ◽  
Decision Making Processes ◽  
Cooperative Decision Making

This chapter introduces an approach, ConSULT (Consensus based on a Shared Understanding of a Leading Topic), to enhance group decision-making processes within organizations. ConSULT provides a computer-mediated framework to allow argumentation, collection and evaluation of discussion and group decision-making. This approach allows for the articulation of all reasoning for and against propositions in a deliberative process that leads to cooperative decision-making. The chapter argues that this approach can enhance group decision-making and can be used in conjunction with any computational intelligence assistance to further enhance its outcome. The approach is particularly applicable in an asynchronous and anonymous environment.

scholarly journals Robust parallel decision-making in neural circuits with nonlinear inhibition

2020 ◽  
Vol 117 (41) ◽  
pp. 25505-25516
Author(s):  
Birgit Kriener ◽  
Rishidev Chaudhuri ◽  
Ila R. Fiete
Keyword(s):  
Decision Making ◽  
Neural Circuits ◽  
Fine Tuning ◽  
Neural Computing ◽  
Hick’S Law ◽  
Noisy Input ◽  
Finite Memory ◽  
Winner Take All ◽  
The Brain ◽  
Take All

An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes∼N⁡log(N)time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick’s law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick’s law may be a symptom of near-optimal parallel decision-making with noisy input.

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