AIMC Topic: Problem Solving
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Solving nonlinear equality constrained multiobjective optimization problems using neural networks.
IEEE transactions on neural networks and learning systems
Jan 30, 2015
This paper develops a neural network architecture and a new processing method for solving in real time, the nonlinear equality constrained multiobjective optimization problem (NECMOP), where several nonlinear objective functions must be optimized in ...
Generative AI without guardrails can harm learning: Evidence from high school mathematics.
Proceedings of the National Academy of Sciences of the United States of America
Jul 1, 2025
Generative AI is poised to revolutionize how humans work, and has already demonstrated promise in significantly improving human productivity. A key question is how generative AI affects learning-namely, how humans acquire new skills as they perform t...
Improving generalization of neural Vehicle Routing Problem solvers through the lens of model architecture.
Neural networks : the official journal of the International Neural Network Society
Jul 1, 2025
Neural models produce promising results when solving Vehicle Routing Problems (VRPs), but may often fall short in generalization. Recent attempts to enhance model generalization often incur unnecessarily large training cost or cannot be directly appl...
A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems.
Evolutionary computation
Mar 15, 2025
Evolutionary Computation (EC) often throws away learned knowledge as it is reset for each new problem addressed. Conversely, humans can learn from small-scale problems, retain this knowledge (plus functionality), and then successfully reuse them in l...
Deliberate Problem-solving with a Large Language Model as a Brainstorm Aid Using a Checklist for Prompt Generation.
The Journal of the Association of Physicians of India
May 1, 2024
Large language models (LLMs) use autoregression to generate text in response to queries. Crafting an appropriate prompt to elicit the desired response from these generative artificial intelligence (AI) models to solve a clinical problem can be a chal...
CLINICAL REASONING AND ARTIFICIAL INTELLIGENCE: CAN AI REALLY THINK?
Transactions of the American Clinical and Climatological Association
Jan 1, 2024
Artificial intelligence (AI) in the form of ChatGPT has rapidly attracted attention from physicians and medical educators. While it holds great promise for more routine medical tasks, may broaden one's differential diagnosis, and may be able to assis...
Two Computational Approaches to Visual Analogy: Task-Specific Models Versus Domain-General Mapping.
Cognitive science
Sep 1, 2023
Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task-specific knowledge acquired from a wealth of prior experience, or is it based on the domain-general manipulat...
Revisiting Transfer Learning Method for Tuberculosis Diagnosis.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2023
Transfer learning (TL) has been proven to be a good strategy for solving domain-specific problems in many deep learning (DL) applications. Typically, in TL, a pre-trained DL model is used as a feature extractor and the extracted features are then fed...
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