Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning: A Study Intended for Large-Scale Video Games

peer reviewedVideo games have become more and more complex over the past decades. Today, players wander in visually and option- rich environments, and each choice they make, at any given time, can have a combinatorial number of consequences. However, modern artificial intelligence is still usually hard-coded, and as the game environments become increasingly complex, this hard-coding becomes exponentially difficult. Recent research works started to let video game autonomous agents learn instead of being taught, which makes them more intelligent. This contribution falls under this very perspective, as it aims to develop a framework for the generic design of autonomous agents for large-scale video games. We consider a class of games for which expert knowledge is available to define a state quality function that gives how close an agent is from its objective. The decision making policy is based on a confidence measurement on the growth of the state quality function, computed by a supervised learning classification model. Additionally, no stratagems aiming to reduce the action space are used. As a proof of concept, we tested this simple approach on the collectible card game Hearthstone and obtained encouraging results

The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study

Context: Machine learning (ML) may enable effective automated test generation. Objective: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges. Methods: We perform a systematic mapping on a sample of 102 publications. Results: ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property-based, and expected output oracles. Supervised learning - often based on neural networks - and reinforcement learning - often based on Q-learning - are common, and some publications also employ unsupervised or semi-supervised learning. (Semi-/Un-)Supervised approaches are evaluated using both traditional testing metrics and ML-related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. Conclusion: Work-to-date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed - and how they are applied - benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.Comment: Under submission to Software Testing, Verification, and Reliability journal. (arXiv admin note: text overlap with arXiv:2107.00906 - This is an earlier study that this study extends

Golf Brain: A Neuropsychological Study of Performance in Competition

Golf, as a sport, has been described by its masters as a mental game first and a technical skill second. Many players logged countless practice hours only to find suboptimal performance in tournaments; when it matters the most. I investigated the relationship between executive functioning specific to decision-making under anxious arousal and golfers’ performance under anxious arousal. I used a repeated measures design including variety of executive functioning tests to examine participants’ abilities. Participants were recruited from western Oregon including collegiate golfers and university students, and were grouped into non-golfers and golfer groups based on whether they played golf and self-reported a consistent ability to score below 80 on a golf course. A golf performance putting task that mimics tournament pressure, “Tornado task,” was the initial task. Heartrate and skin conductance data were gathered during the Tornado task and executive functioning tasks. Results showed differences between golfer and non-golfers in their physiological arousal during risk-reward decisions. The IGT-2, Color-Word, and Tower Test executive functioning measures yielded similar arousal levels between groups. Self-reported anxiety on performance did not equate with greater physiological arousal during executive functioning tasks. RMSSD appears to be a more accurate measure of physiological arousal under pressure than EDA. It is likely that golfers have more training in managing sympathetic arousal in competition, are more accustomed to risk reward situations, and take greater risks in the presence of physiological arousal. I found golfers experience less anxious arousal while taking executive functioning tasks, and take more risks in decision-making decisions yet do not outperform non-golfers. Golfers were able to manage their nervous system arousal more effectively than non-golfers

Special Topics in Information Technology

This open access book presents outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the best theses defended in 2021-22 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

Theoretical and Practical Advances in Computer-based Educational Measurement

This open access book presents a large number of innovations in the world of operational testing. It brings together different but related areas and provides insight in their possibilities, their advantages and drawbacks. The book not only addresses improvements in the quality of educational measurement, innovations in (inter)national large scale assessments, but also several advances in psychometrics and improvements in computerized adaptive testing, and it also offers examples on the impact of new technology in assessment. Due to its nature, the book will appeal to a broad audience within the educational measurement community. It contributes to both theoretical knowledge and also pays attention to practical implementation of innovations in testing technology

Special Topics in Information Technology

This open access book presents outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the best theses defended in 2021-22 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

EVALUATING ARTIFICIAL INTELLIGENCE METHODS FOR USE IN KILL CHAIN FUNCTIONS

Current naval operations require sailors to make time-critical and high-stakes decisions based on uncertain situational knowledge in dynamic operational environments. Recent tragic events have resulted in unnecessary casualties, and they represent the decision complexity involved in naval operations and specifically highlight challenges within the OODA loop (Observe, Orient, Decide, and Assess). Kill chain decisions involving the use of weapon systems are a particularly stressing category within the OODA loop—with unexpected threats that are difficult to identify with certainty, shortened decision reaction times, and lethal consequences. An effective kill chain requires the proper setup and employment of shipboard sensors; the identification and classification of unknown contacts; the analysis of contact intentions based on kinematics and intelligence; an awareness of the environment; and decision analysis and resource selection. This project explored the use of automation and artificial intelligence (AI) to improve naval kill chain decisions. The team studied naval kill chain functions and developed specific evaluation criteria for each function for determining the efficacy of specific AI methods. The team identified and studied AI methods and applied the evaluation criteria to map specific AI methods to specific kill chain functions.Civilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCaptain, United States Marine CorpsCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited