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Artificial Intelligence

Artificial Intelligence problem: Intelligent Optimization

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Literature Review: Intelligent Optimization problem for Artificial Intelligence

This “Ten Problems for Artificial Intelligence in the 2020s” booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.

Without further ado, these are the ten problems that we are going to introduce in this booklet:

  1. computer games, 
  2. intelligent optimization,
  3. accountability,
  4. human intervention,
  5. ethical issues,
  6. bias,
  7. interpretability,
  8. software development,
  9. open source,
  10. operations research.

Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.

The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.


2 Intelligent Optimization

THE PROBLEM — Optimization is the maximization or minimization of a variable for an objective model. It can be performed through traditional, mathematical rules or, more recently, making use of artificial intelligence methods, that is starting from randomness, chance factor and heuristic approaches. Several industries make use of optimization methods already and are now transitioning toward intelligent optimization, provided that their business case for increasing work complexity is sound.

CASE STUDIES — … buy this booklet from Amazon …

CONCLUSIONS — Intelligent optimization done by inspiring from nature and swarms had its own unique scientific literature, with effective solutions provided for optimization problems from different fields. Scientists seek theoretical insights and demand a sound experimental methodology for evaluating algorithms. Practical applications of algorithm configuration are prone to several (often subtle) pitfalls in the experimental design that can render the procedure ineffective. Prediction and supplier selection are frequently addressed by using artificial intelligence in the supply chain context. The particle swarm optimization algorithm combined model is the optimal forecasting model for iron ore demand in China. Nonlinear neural controllers and intelligent algorithms like Particle Swarm Optimization and Artificial Bee Colony can guide an autonomous mobile robot during continuous path-tracking. Based on artificial intelligence technology, hull form optimization can effectively improve its efficiency and provide key technical support for ship intelligent optimization. The application of data mining in drilling, completion, and surface facility engineering etc. has resulted in intelligent equipment and integrated software. Artificial intelligence can generate predictive and actionable insights that will help make better cybersecurity decisions and protect smart grids against threats. A study confirms that the Group Method of Data Handling algorithm gives proper results for the performance of Water Distribution Networks.

TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …


ArtifIntel
“Ten Problems for Artificial Intelligence in the 2020s” booklet for Amazon Kindle, 2020; click on the cover to go to the dedicated Amazon listing page

booklet updated on 19 Jun 2021, now on sale as version 1.2

By TenProblems

Literature Reviews for Inquisitive Minds