Artificial Intelligence and Cognitive Science

Editorial for 17th AICS Conference

Once a Criminal, Always a Criminal?: ‘Redeemability’ and the Psychology of Punitive Public Attitudes

Several studies have sought to link punitive public attitudes to attribution style and/or lay theories of crime. This research finds that those who believe criminal acts are the result of freely chosen and willful behavior are more likely to be punitive than those who feel crime is the result of external circumstances and constraints. These analyses focus on only one dimension of attributions: locus of control (internal/external). In this analysis, we include a second dimension, thought to be a better predictor of attitudes in social psychological research: stability/instability. In addition to measuring lay theories of crime causation, we also test for “belief in redeemability” (or beliefs about the ability of deviants to change their ways). Our hypothesis is that this other dimension of personal attributions (stability/instability) may be as critical in explaining support for highly punitive criminal justice policies as beliefs about criminal responsibility. We find evidence supportive of this model in an analysis of data from postal survey of residents of six areas in England.

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.

Forensic Geology in Environmental Crime: Illegal Waste Movement Burial in Northern Ireland

The illegal burial of waste often occurs in locations where loose, transferable material is abundant, allowing covert pits to be dug or filled. The transfer of waste material onto suspects and their vehicles during loading, unloading, and burial is common, as is the case during other criminal activities such as the burial of murder victims. We use two case studies to show that the established principles of using geological materials in excluding or linking suspects can be applied to illegal waste disposal. In the first case, the layering of different geological materials on the tailgate of a container used to transport toxic waste demonstrated where the vehicle had been and denied the owner's alibi, associating him with an illegal dumpsite. In the second case, an unusual suite of minerals, recovered from a suspect's trousers, provided the intelligence that led environmental law enforcement officers to an illegal waste burial site.