AIMC Topic: Problem Solving

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Utilizing artificial intelligence to solving time - cost - quality trade-off problem.

Scientific reports
This study presents the Slime Mold Algorithm (SMA) to solve the time-cost-quality trade-off problem in a construction project. The proposed SMA is a flexible and efficient algorithm in exploration and exploitation to reach the best optimal solution t...

Deep Learning Model for the Image Fusion and Accurate Classification of Remote Sensing Images.

Computational intelligence and neuroscience
Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote ...

Multiagent-Based Data Presentation Mechanism for Multifaceted Analysis in Network Management Tasks.

Sensors (Basel, Switzerland)
Although network management tasks are highly automated using big data and artificial intelligence technologies, when an unforeseen cybersecurity problem or fault scenario occurs, administrators sometimes directly analyze system data to make a heurist...

Capturing advanced human cognitive abilities with deep neural networks.

Trends in cognitive sciences
How can artificial neural networks capture the advanced cognitive abilities of pioneering scientists? I suggest they must learn to exploit human-invented tools of thought and human-like ways of using them, and must engage in explicit goal-directed pr...

Neurodynamics-driven portfolio optimization with targeted performance criteria.

Neural networks : the official journal of the International Neural Network Society
This paper addresses portfolio selection with targeted performance criteria via neurodynamic optimization. Five portfolio optimization problems are formulated with a variable weight to maximize five risk-adjusted performance criteria in Markowitz's m...

A Barrier Varying-Parameter Dynamic Learning Network for Solving Time-Varying Quadratic Programming Problems With Multiple Constraints.

IEEE transactions on cybernetics
Many scientific research and engineering problems can be converted to time-varying quadratic programming (TVQP) problems with constraints. Thus, TVQP problem solving plays an important role in practical applications. Many existing neural networks, su...

Computational Scientific Discovery in Psychology.

Perspectives on psychological science : a journal of the Association for Psychological Science
Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in ...

Explaining Aha! moments in artificial agents through IKE-XAI: Implicit Knowledge Extraction for eXplainable AI.

Neural networks : the official journal of the International Neural Network Society
During the learning process, a child develops a mental representation of the task he or she is learning. A Machine Learning algorithm develops also a latent representation of the task it learns. We investigate the development of the knowledge constru...

A smoothing gradient-based neural network strategy for solving semidefinite programming problems.

Network (Bristol, England)
Linear semidefinite programming problems have received a lot of attentions because of large variety of applications. This paper deals with a smooth gradient neural network scheme for solving semidefinite programming problems. According to some proper...

A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level.

Proceedings of the National Academy of Sciences of the United States of America
We demonstrate that a neural network pretrained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI's...