Training Competences in Industrial Risk Prevention with Lego (R) Serious Play (R): A Case Study

This paper proposes the use of the Lego (R) Serious Play (R) (LSP) methodology as a facilitating tool for the introduction of competences for Industrial Risk Prevention by engineering students from the industrial branch (electrical, electronic, mechanical and technological engineering), presenting the results obtained in the Universities of Cadiz and Seville in the academic years 2017-2019. Current Spanish legislation does not reserve any special legal attribution, nor does it require specific competence in occupational risk prevention for the regulated profession of a technical industrial engineer (Order CIN 351:2009), and only does so in a generic way for that of an industrial engineer (Order CIN 311:2009). However, these universities consider the training in occupational health and safety for these future graduates as an essential objective in order to develop them for their careers in the industry. The approach is based on a series of challenges proposed (risk assessments, safety inspections, accident investigations and fire protection measures, among others), thanks to the use of "gamification" dynamics with Lego (R) Serious Play (R). In order to carry the training out, a set of specific variables (industrial sector, legal and regulatory framework, business organization and production system), and transversal ones (leadership, teamwork, critical thinking and communication), are incorporated. Through group models, it is possible to identify dangerous situations, establish causes, share and discuss alternative proposals and analyze the economic, environmental and organizational impact of the technical solutions studied, as well as take the appropriate decisions, in a creative, stimulating, inclusive and innovative context. In this way, the theoretical knowledge which is acquired is applied to improve safety and health at work and foster the prevention of occupational risks, promoting the commitment, effort, motivation and proactive participation of the student teams

A scouting strategy for real-time strategy games

© 2014 ACM. Real-time strategy (RTS) is a sub-genre of strategy video games. RTS games are more realistic with dynamic and time-constraint game playing, by abandoning the turn-based rule of its ancestors. Playing with and against computer-controlled players is a pervasive phenomenon in RTS games, due to the convenience and the preference of groups of players. Hence, better game-playing agents are able to enhance game-playing experience by acting as smart opponents or collaborators. One-way of improving game-playing agents' performance, in terms of their economic-expansion and tactical battlefield-arrangement aspects, is to understand the game environment. Traditional commercial RTS game-playing agents address this issue by directly accessing game maps and extracting strategic features. Since human players are unable to access the same information, this is a form of "cheating AI", which has been known to negatively affect player experiences. Thus, we develop a scouting mechanism for RTS game-playing agents, in order to enable game units to explore game environments automatically in a realistic fashion. Our research is grounded in prior robotic exploration work by which we present a hierarchical multi-criterion decision-making (MCDM) strategy to address the incomplete information problem in RTS settings

Cybersecurity Mindfulness in the Age of Mindless AIs: Investigating AI Assistants Impact in High-Reliability Organizations

The Focus: The focus of this Master Thesis is to investigate how AI tools, such as Large Learning Models (LLMs), impact cybersecurity operations in organizations that are regarded as highly reliable. To understand the impacts of AI tools on such operations, we also need to understand the nature of AI tools, their context of use and the experience of users that rely on them. Research Approach: This thesis is structured around two different methods of investigation. First a systematic literature review was conducted, where related articles was found in different databases, i.e. Google Scholar, Web of Science and the Basket of Eight publications. After this a Qualitative study was conducted where a multiple case study with interviews and random sampling was utilized. A total of 8 informants were interviewed for this study, each lasting ~30 minutes where the questions were based on the findings from the literature. Findings: From the literature it became clear that AIs, while better than humans in many things such as analyzing Big Data, intrusion detection and other pattern recognition activities, does bring with it many difficulties to the individual and the organization. AIs and LLMs are prone to making you develop an overreliance on them where you accept their answers because of your own biases, while the information itself might be fundamentally wrong or even deceitful. This phenomenon is called AI Hallucination and is vital to understanding an AIs effect on individuals. The literature highlighted that when using any tool, it was important to realize that the AI tool is simply a machine and might be wrong, question everything and do not accept any information at face value. Quite simply, think things through. LLMs have a problem with transparency, it is impossible to know its ‘reasoning’ behind the information it provides. This fact is supported by both the literature and the interviews themselves. Overreliance, hallucination, cultivating the wrong kind of trust and lack of transparency all lead to an individual acting mindless who takes the information as true. While they have been deceived by trusting something that essentially is untrustworthy or at the very least should have been looked more into. Implication: The practical implications for this study is that an organization, especially if it is of high reliability should carefully identify measures to avoid the negative impact of AI Assistants when used in day-to-day work in cybersecurity operations

Knowledge Acquisition Analytical Games: games for cognitive systems design

Knowledge discovery from data and knowledge acquisition from experts are steps of paramount importance when designing cognitive systems. The literature discusses extensively on the issues related to current knowledge acquisition techniques. In this doctoral work we explore the use of gaming approaches as a knowledge acquisition tools, capitalising on aspects such as engagement, ease of use and ability to access tacit knowledge. More specifically, we explore the use of analytical games for this purpose. Analytical game for decision making is not a new class of games, but rather a set of platform independent simulation games, designed not for entertainment, whose main purpose is research on decision-making, either in its complete dynamic cycle or a portion of it (i.e. Situational Awareness). Moreover, the work focuses on the use of analytical games as knowledge acquisition tools. To this end, the Knowledge Acquisition Analytical Game (K2AG) method is introduced. K2AG is an innovative game framework for supporting the knowledge acquisition task. The framework introduced in this doctoral work was born as a generalisation of the Reliability Game, which on turn was inspired by the Risk Game. More specifically, K2AGs aim at collecting information and knowledge to be used in the design of cognitive systems and their algorithms. The two main aspects that characterise those games are the use of knowledge cards to render information and meta-information to the players and the use of an innovative data gathering method that takes advantage of geometrical features of simple shapes (e.g. a triangle) to easily collect players\u2019 beliefs. These beliefs can be mapped to subjective probabilities or masses (in evidence theory framework) and used for algorithm design purposes. However, K2AGs might use also different means of conveying information to the players and to collect data. Part of the work has been devoted to a detailed articulation of the design cycle of K2AGs. More specifically, van der Zee\u2019s simulation gaming design framework has been extended in order to account for the fact that the design cycle steps should be modified to include the different kinds of models that characterise the design of simulation games and simulations in general, namely a conceptual model (platform independent), a design model (platform independent) and one or more implementation models (platform dependent). In addition, the processes that lead from one model to the other have been mapped to design phases of analytical wargaming. Aspects of game validation and player experience evaluation have been addressed in this work. Therefore, based on the literature a set of validation criteria for K2AG has been proposed and a player experience questionnaire for K2AGs has been developed. This questionnaire extends work proposed in the literature, but a validation has not been possible at the time of writing. Finally, two instantiations of the K2AG framework, namely the Reliability Game and the MARISA Game, have been designed and analysed in details to validate the approach and show its potentialities

A cloud-based path-finding framework: Improving the performance of real-time navigation in games

This paper reviews current research in Cloud utilisation within games and finds that there is little beyond Cloud gaming and Cloud MMOs. To this end, a proof-of-concept Cloud-based Path-finding framework is introduced. This was developed to determine the practicality of relocating the computation for navigation problems from consumer-grade clients to powerful business-grade servers, with the aim of improving performance. The results gathered suggest that the solution might be impractical. However, because of the poor quality of the data, the results are largely inconclusive. Thus recommendations and questions for future research are posed.N/