Artificial Intelligence and Cognitive Science

Editorial for 17th AICS Conference

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

The relationship between counterfactual thinking and emotional reactions to event outcomes: Does one account fit all?

By enabling a comparison between what is and what might have been, counterfactual thoughts amplify our emotional responses to bad outcomes. Well-known demonstrations such as the action effect (the tendency to attribute most regret to a character whose actions brought about a bad outcome) and the temporal order effect (the tendency to undo the last in a series of events leading up to a bad outcome) are often explained in this way. An important difference between these effects is that outcomes are due to decisions in the action effect, whereas in the temporal order effect outcomes are achieved by chance. In Experiment 1, we showed that imposing time pressure leads to a significant reduction in the action but not in the temporal order effect. In Experiment 2, we found that asking participants to evaluate the protagonists (