scholarly journals A Neural Network Auction For Group Decision Making Over a Continuous Space

2021 ◽  
Author(s):  
Yoram Bachrach ◽  
Ian Gemp ◽  
Marta Garnelo ◽  
Janos Kramar ◽  
Tom Eccles ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Group Decision ◽  
Network Models ◽  
Individual Utility ◽  
Neural Network Models ◽  
Continuous Space ◽  
Total Utility ◽  
Pricing Rule ◽  
True Preferences

We propose a system for conducting an auction over locations in a continuous space. It enables participants to express their preferences over possible choices of location in the space, selecting the location that maximizes the total utility of all agents. We prevent agents from tricking the system into selecting a location that improves their individual utility at the expense of others by using a pricing rule that gives agents no incentive to misreport their true preferences. The system queries participants for their utility in many random locations, then trains a neural network to approximate the preference function of each participant. The parameters of these neural network models are transmitted and processed by the auction mechanism, which composes these into differentiable models that are optimized through gradient ascent to compute the final chosen location and charged prices.

scholarly journals Prediction of weld quality using intelligent decision making tools

2012 ◽  
Vol 1 (2) ◽  
pp. 131 ◽  
Author(s):  
Edwin Raja Dhas ◽  
Somasundaram Kumanan ◽  
C.P. Jesuthanam
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Regression Model ◽  
Back Propagation ◽  
Network Models ◽  
Multiple Regression Model ◽  
Decision Making Process ◽  
Weld Quality ◽  
Neural Network Models ◽  
Intelligent Decision Making

Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design anddemand of quality products. To make decision making process online, effective and efficient artificial intelligent tools likeneural networks are being attempted. This paper proposes the development of neural network models for prediction ofweld quality in Submerged Arc Welding (SAW). Experiments are designed according to Taguchi’s principles andmathematical equations are developed using multiple regression model. Proposed neural network models are developedusing experimental data, supported with the data generated by regression model. The performances of the developedmodels are compared in terms of computational speed and prediction accuracy. It is found that Neural Network trainedwith Particle Swarm Optimization (NNPSO) performs better than Neural Network trained with Back Propagation (BPNN)algorithm, Radial Basis Functional Neural Network (RBFNN) and Neural Network trained with Genetic Algorithm(NNGA). The developed scheme for weld quality prediction is flexible, competent, and accurate than existing models andit scopes better online monitoring system. Finally the developed models are validated. The proposed and developedtechnique finds a good scope and a better future in the relevant field where human can avoid unwanted risks duringoperations with the deployment of robots.

scholarly journals Symbolic-Connectionist Representational Model for Optimizing Decision Making Behavior in Intelligent Systems

2018 ◽  
Vol 8 (1) ◽  
pp. 326
Author(s):  
R. John Martin ◽  
Sujatha Sujatha
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Intelligent Agents ◽  
Intelligent Systems ◽  
Process Model ◽  
Cognitive Model ◽  
Network Models ◽  
Feed Forward Neural Network ◽  
Neural Network Models ◽  
Representational Model

Modeling higher order cognitive processes like human decision making come in three representational approaches namely symbolic, connectionist and symbolic-connectionist. Many connectionist neural network models are evolved over the decades for optimizing decision making behaviors and their agents are also in place. There had been attempts to implement symbolic structures within connectionist architectures with distributed representations. Our work was aimed at proposing an enhanced connectionist approach of optimizing the decisions within the framework of a symbolic cognitive model. The action selection module of this framework is forefront in evolving intelligent agents through a variety of soft computing models. As a continous effort, a Connectionist Cognitive Model (CCN) had been evolved by bringing a traditional symbolic cognitive process model proposed by LIDA as an inspiration to a feed forward neural network model for optimizing decion making behaviours in intelligent agents. Significanct progress was observed while comparing its performance with other varients.

Applying artificial neural network models to clinical decision making.

2000 ◽  
Vol 12 (1) ◽  
pp. 40-51 ◽  
Author(s):  
Rumi Kato Price ◽  
Edward L. Spitznagel ◽  
Thomas J. Downey ◽  
Donald J. Meyer ◽  
Nathan K. Risk ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Clinical Decision Making ◽  
Network Models ◽  
Clinical Decision ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods

2022 ◽  
pp. 1052-1076
Author(s):  
Eslam Mohammed Abdelkader ◽  
Mohamed Marzouk ◽  
Tarek Zayed
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Ground Penetrating Radar ◽  
Polynomial Regression ◽  
Bridge Decks ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Ground Penetrating

Bridges are aging and deteriorating. Thus, the development of Bridge Management Systems (BMSs) became imperative nowadays. Condition assessment is one of the most critical and vital components of BMSs. Ground Penetrating Radar (GPR) is one of the non-destructive techniques (NDTs) that are used to evaluate the condition of bridge decks which are subjected to the rebar corrosion. The objective of the proposed method is to develop standardized amplitude scale for bridge decks based on a hybrid optimization-decision making model. Shuffled frog leaping algorithm is employed to compute the optimum thresholds. Then, polynomial regression and artificial neural network models are designed to predict the prioritizing index based on a set of multi-criteria decision-making methods. The weibull distribution is utilized to capture the stochastic nature of deterioration of concrete bridge decks. Lastly, a case study is presented to demonstrate the capabilities of the proposed method.

scholarly journals Predicting Crime and Other Uses of Neural Networks in Police Decision Making

2021 ◽  
Vol 12 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Law Enforcement ◽  
Prediction Models ◽  
Network Models ◽  
Time Of Day ◽  
Neural Network Models ◽  
Police Decision Making ◽  
The Neural Network

Neural networks are a machine learning method that excel in solving classification and forecasting problems. They have also been shown to be a useful tool for working with big data oriented environments such as law enforcement. This article reviews and examines existing research on the utilization of neural networks for forecasting crime and other police decision making problem solving. Neural network models to predict specific types of crime using location and time information and to predict a crime’s location when given the crime and time of day are developed to demonstrate the application of neural networks to police decision making. The neural network crime prediction models utilize geo-spatiality to provide immediate information on crimes to enhance law enforcement decision making. The neural network models are able to predict the type of crime being committed 16.4% of the time for 27 different types of crime or 27.1% of the time when similar crimes are grouped into seven categories of crime. The location prediction neural networks are able to predict the zip code location or adjacent location 31.2% of the time.

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