The Future of Neuroimaged Lie Detection and the Law

News broke during the last week of September 2008 that the Department of Homeland Security has begun testing a new mind-reading device. According to the Department’s website, the Human Factors Directorate of Science and Technology has been working on Future Attribute Screening Technology (FAST). Although design specifics have not been made public, the MALINTENT prototype is a device that rapidly and remotely measures subjects’ body temperature, heart rate, and respiration. MALINTENT then compares these measurements with a matrix of physiological norms to generate conclusions about each subject’s future dangerousness. MALINTENT’s designers and proponents believe that this new technology will reliably distinguish perspiring perambulators and fearful flyers from true terrorists. These claims are difficult to assess because the results of the only publically disclosed tests of MALINTENT were subsequently classified

Planning Graph Heuristics for Belief Space Search

Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning

Forensic science and juror decision making: can jurors be taught to recognise bias?

Forensic science plays a central role in the administration of judicial and legal processes, and crime scene investigations. Fingerprint, bite-mark, and DNA comparisons, as well as analyses of audio and video recordings, have proven invaluable to investigators and legal practitioners. Yet research has indicated that forensic science is not infallible, and that the strength and validity of forensic evidence is often overstated. Studies have found that a range of cognitive biases, including contextual and motivational biases, may substantially influence forensic procedure. Authors of the 2016 President’s Council of Advisors on Science and Technology (PCAST) report on forensic comparison evidence argue that issues affecting forensic science are considerable, and will likely continue to influence forensic evidence admitted into courts. Technological advances have not served to reduce incidents of error attributable to bias as outcomes of analyses still rely on human judgement. Recommendations for strategies to reduce bias in forensic labs have been identified; however, researchers have stressed that implementing these may only gradually reduce instances of questionable forensic evidence entering courtrooms. Judges have been assigned a gatekeeper role in determining the admittance of forensic evidence into court; however, research has found that they are ill-equipped to reliably do so. This leaves jurors with the responsibility to identify and critique weak or flawed forensic evidence. However, studies have shown that jurors are prone to erroneous decision making. In light of this, it is troubling that literature has paid limited attention to the impact of forensic bias on court proceedings. Jurors are often influenced by extraneous information such as demographic characteristics of a defendant and victim, and the nature of a crime. They have also been found to overestimate their comprehension of forensic evidence. Attempts to improve juror decision making have included the introduction of supplementary materials such as juror instructions and forensic reports. However, such tools have not been empirically supported. Found and Edmond (2012) proposed a forensic report format to address the limitations of traditional forensic reports for effectively conveying information to jurors. Claims about the efficacy of their proposed format have not been substantiated, and cognitive psychology literature and current juror decision making research do not support Found and Edmond’s (2012) conclusions. The pervasive use of forensic evidence in court proceedings has implications for strategies to address the effects of cognitive biases on judicial outcomes. Unfortunately, current materials have not been shown to improve jurors’ evaluations of evidence, pointing to a critical gap in literature on juror information processing and decision making. This thesis will therefore attempt to empirically evaluate Found and Edmond’s (2012) proposed report format. It will also explore research on juror decision making in the context of forensic and other evidence. Differences between current juror information processing paradigms will be investigated in order to determine whether they effectively account for the complexities of juror behaviour. Furthermore this thesis will contribute new perspectives to the field of juror research by exploring alternative approaches to improving the accuracy and reliability of juror information processing and decision making outcomes.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Psychology, 201

Aprendizaje por Refuerzo: Fundamentos Teóricos y Aplicación al cubo de Rubik

Trabajo de Fin de Doble Grado en Ingeniería Informática y Matemáticas, Facultad de Informática UCM, Departamento de Sistemas Informáticos y Computación, Curso 2021/2022.The techniques employed and developed in the area of reinforcement learning have been evolving since their origins at the end of the 20th century. Thanks to the various advances in this field, it has been possible to solve increasingly complicated problems. The influence of other areas of machine learning and artificial intelligence has enabled applications of reinforcement learning that initially posed great challenges due to their computational requirements. One such problem is the one we will discuss in this work, which is characterized by a large state space and a single final state. First, a theoretical introduction to the area of reinforcement learning will be given, focusing on those aspects most relevant to the solution of our problem. Then, a theoretical description of the DeepCubeA algorithm will be presented, that was designed to solve the Rubik’s 3x3x3 Cube, which has a large state space and only one final state. Finally, we will design and implement a version of the DeepCubeA algorithm, adding some relevant aspects of its previous version (DeepCube), and we will study its behavior with the Rubik’s 2x2x2 and 3x3x3 Cubes, and the Hanoi Towers.Las técnicas utilizadas y desarrolladas en el área del aprendizaje por refuerzo han ido evolucionando desde sus inicios, a finales del siglo XX. Gracias a los distintos avances en este sector, se han podido resolver problemas cada vez más complicados. La influencia de otras áreas del aprendizaje automático y de la inteligencia artificial han permitido aplicaciones del aprendizaje por refuerzo que inicialmente suponían grandes desafíos por sus requerimientos computacionales. Uno de esos problemas es el que trataremos en este trabajo, que se caracteriza por un gran espacio de estados y un único estado final. En un primer lugar, se dará una introducción teórica al área del aprendizaje por refuerzo, centrándonos en aquellos aspectos más relevantes en la resolución de nuestro problema. Después, se expondrá una descripción teórica del algoritmo DeepCubeA que fue diseñado especialmente para resolver el cubo de Rubik 3x3x3, caracterizado precisamente por un gran espacio de estados y un único estado final. Por último, diseñaremos e implementaremos una versión del algoritmo DeepCubeA, añadiendo algunos aspectos relevantes de su version anterior (DeepCube), y estudiaremos su comportamiento con los cubos de Rubik 2x2x2 y 3x3x3, y las Torres de Hanói.Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaTRUEunpu

A Rational Scheme for Conflict Detection and Resolution in Distributed Collaborative Environments for Enterprise Integration

A typical enterprise may have large numbers of information sources such as data stores, expert systems, knowledge-based systems, or standard software systems. These may need to be integrated so that, for example, an application program or a decision maker can access information from all these sources. Such architectures are generally called 'Distributed Collaborative Environments for Enterprise Integration'. A general problem in these enterprise integration architectures is that information from heterogeneous, pre-existing sources may be obsolete, incomplete, incorrect or, for many other reasons, contradictory. Thus, conflicting results may occur when the same information is requested from semantically related sources. A mechanism is required to detect and resolve these conflicts in a way that is rational to any potential client of the integration environment. This thesis lays open the design of a general mechanism for conflict detection and resolution that enables intelligent information agents to reason about contradictory information from pre-existing, heterogeneous and autonomous sources. The mechanism's theoretical basis is a framework that is drawn from evidence law, which shares some fundamental commonalities with conflict detection and resolution in enterprise integration environments. Conflict detection opens with gathering the results collected by the information retrieval process. These results may have justifications or certainty assessments attached to them. Furthermore, it identifies whether and how these results are conflicting. The design of a conflict resolution mechanism is based on a rational scheme for judging the weight of conflicting results. First, the agents assess the reliability or credibility of an information source. Judgement based on the weight of conflicting results is first applied to any available, domain-specific, resolution strategies. Second, the agent applies any 'general scientific' resolution strategies that are not specific to one domain. When no domain-related expertise can solve the conflict then the agent can only judge on domain independent evaluation criteria such as the results' reliability. A scheme is sketched out for judgement based on the reliability of conflicting results, involving three steps: Ranking the conflicting results according to their reliability; Ways to redefine conflicting results; and Heuristic decision-making. The evaluation includes a computational implementation of an enterprise integration environment incorporating a model of an information agent. An example is realised in this environment. The conflict detection and resolution mechanism, and interfaces to each integrated source, are implemented in Visual C++. A case study is conducted on this scenario to evaluate each conflict detection and resolution step. Furthermore, this illustrates both the advantages over existing approaches and the limitations

Considering forensic science: juror decision making and unvalidated identification evidence

Rapid scientific advances mean that new techniques and areas of research are being used by crime labs to test forensic evidence, but as innovations grow, so does fear that invalid science will make its way to the courtroom. If jurors and judges are not informed of the threats to validity that are associated with identification evidence they are at risk of overestimating the reliability of that evidence. The overarching goal of this thesis was to investigate whether scientifically informed opposing expert testimony or cross-examination will educate jurors about unreliable forensic science, and whether there are individual differences that will affect the perception of forensic evidence. Study one investigated whether opposing expert testimony could educate jurors about anthropometric facial comparison evidence. In addition, participants’ scores on measures of epistemological sophistication and argument skill were used to test for direct effects on verdict, and indirect effects through ratings on a measure of methodological reliability. Path analysis did not show support for relationship for the two individual difference measures. Opposing expert testimony was able to reduce ratings of the reliability of the forensic evidence, indicating that presenting participants with criticisms of unreliable forensic evidence is capable of informing jurors of limitations. Study two aimed to replicate the main findings of the first study while testing a different measure of individual difference: bias towards forensic evidence, as captured by the Forensic Evidence Evaluation Bias Scale (FEEBS, Smith & Bull, 2012, 2014). Opposing expert testimony reduced reliability, and scores on the pro-prosecution subscale of the FEEBS led to higher ratings of reliability, indicating that when the participants were predisposed to see forensic evidence as highly trustworthy and conclusive they were more likely to convict. Qualitative analysis of responses justifying verdict choice showed that opposing expert testimony was informative, but that many participants struggled with understanding scientific methodology and had unreasonable expectations about forensic science. Study three tested whether scientifically-informed cross-examination would lead to reduced reliability. Three types of forensic identification sciences were used: anthropometric facial comparison, fingerprint, and voice identification. Participants read through expert testimony regarding one of the three types of evidence, and then either scientifically-informed cross-examination, or questions that focussed on the qualifications and experience of the expert. Multi-group analyses and individual path analyses were conducted. Only the relationship between examination type and evidence type was different between groups, and scientifically-informed cross-examination did not affect ratings of reliability. Scores on the FEEBS affect neither reliability nor verdict. This suggests that differences in testimony and either the origin, or complexity, of criticisms towards evidence may have a large impact on verdict. This thesis contributes to furthering our understanding of juror decision making regarding unreliable forensic evidence as it has demonstrated that perception of reliability, even if based on substantially biased or incorrect reasoning, will have the largest impact on verdict choice. The findings will be useful to researchers looking into the best ways of educating jurors and judges, as well as those calling for validation studies of forensic sciences.Thesis (Ph.D.) -- University of Adelaide, School of Psychology, 201