Adventures in the Classroom Creating Role-Playing Games Based on Traditional Stories for the High School Curriculum

The goal of this thesis is to develop a template for turning traditional stories into role-playing games for the high school curriculum. By developing 3 sample games based on Greek mythology, Arthurian legends, and a widespread folktale type, I explored the process of creating games that fit the limits of secondary classrooms and can be used to address specific educational standards. The sample games were tested with groups of high school and college students, and the results of the testing sessions evaluated in a narrative case study format. Feedback from the testing sessions was incorporated in the template, the final product of the thesis project. By exploring tabletop role-playing as a form of emergent interactive storytelling, a connection has been created between traditional storytelling and popular culture with the hope of reaching out to new audiences and introducing a stronger interactive element into storytelling in secondary education

Competitor analysis of functional group H-bond donor and acceptor properties using the Cambridge Structural Database.

Intermolecular interactions found in the Cambridge Structural Database (CSD) are analysed as the outcomes of competitions between the different functional groups that are present in each structure: the most energetically favourable interactions are expected to win more often than weaker interactions. Tracking winners and losers through each crystal structure in the CSD provides data that can be analysed using paired comparison algorithms to rank functional group H-bonding properties based on how frequently they outcompete other functional groups in the crystal. This treatment is superior to simple statistical analyses of whether functional groups H-bond or not, because the distribution of H-bond donors and acceptors in the structures of the molecules found in the CSD is non-random. Most organic molecules contain more acceptors than donors, so that all H-bond donors are almost always H-bonded in all crystal structures, and most acceptors are not. The rankings of H-bond acceptors obtained by applying the TrueSkill paired comparison algorithm to the CSD agree well with the corresponding experimentally determined solution phase H-bond acceptor parameters β, but there is insufficient data to corroborate H-bond donor rankings calculated in the same way. The method is used to make predictions of the H-bond acceptor properties of functional groups for which solution phase measurements are not available.Engineering and Physical Sciences Research Council, Cambridge Crystallographic Data Centr

The Beauty of the Game

Imagine a deep philosophical conversation about a beautiful shot by a college player in a Final Four basketball game

Multi Agent Reinforcement Learning for smart mobility and traffic scenarios

openAutonomous Driving is one of the most fascinating and stimulating field in modern engineering. While some partial autonomous cars already exist in the industrial market, they are far from being completely independent in any situation. One of the things that these vehicles lack the most is the ability of handling traffic scenarios and situations in which interaction with other road users is required. The purpose of this work is that of investigating learning techniques that could be exploited in order to face the challenge described above. The focus will be put on the Multi-Agent Reinforcement Learning (MARL) paradigm, which seems particularly appropriate to address this kind of problem, given its ability to learn and solve complex tasks without any prior knowledge requirement. The MARL paradigm has its roots both in the classical single agent Reinforcement Learning setup, but also in the Game Theory field. For this reason, after an introduction to the problem and some literature review of related works, the project will begin with an introduction to those two topics. After that the MARL paradigm will be analyzed, focusing both on the theoretical aspects and on the algorithmic point of view. The thesis will then proceed with an experimental section, where some of the state-of-the-art MARL algorithms will be adapted to the Autonomous Driving setup and tested, making use of a simulator, called SMARTS, specifically developed for this purpose. To conclude this work the results obtained in simulation will be analyzed and discussed, and also some ideas for future development will be presented.Autonomous Driving is one of the most fascinating and stimulating field in modern engineering. While some partial autonomous cars already exist in the industrial market, they are far from being completely independent in any situation. One of the things that these vehicles lack the most is the ability of handling traffic scenarios and situations in which interaction with other road users is required. The purpose of this work is that of investigating learning techniques that could be exploited in order to face the challenge described above. The focus will be put on the Multi-Agent Reinforcement Learning (MARL) paradigm, which seems particularly appropriate to address this kind of problem, given its ability to learn and solve complex tasks without any prior knowledge requirement. The MARL paradigm has its roots both in the classical single agent Reinforcement Learning setup, but also in the Game Theory field. For this reason, after an introduction to the problem and some literature review of related works, the project will begin with an introduction to those two topics. After that the MARL paradigm will be analyzed, focusing both on the theoretical aspects and on the algorithmic point of view. The thesis will then proceed with an experimental section, where some of the state-of-the-art MARL algorithms will be adapted to the Autonomous Driving setup and tested, making use of a simulator, called SMARTS, specifically developed for this purpose. To conclude this work the results obtained in simulation will be analyzed and discussed, and also some ideas for future development will be presented

Global Optimization of Minority Game by Smart Agents

We propose a new model of minority game with so-called smart agents such that the standard deviation and the total loss in this model reach the theoretical minimum values in the limit of long time. The smart agents use trail and error method to make a choice but bring global optimization to the system, which suggests that the economic systems may have the ability to self-organize into a highly optimized state by agents who are forced to make decisions based on inductive thinking for their limited knowledge and capabilities. When other kinds of agents are also present, the experimental results and analyses show that the smart agent can gain profits from producers and are much more competent than the noise traders and conventional agents in original minority game.Comment: 5 pages, 5 figure

Social Media in the Dental School Environment, Part B: Curricular Considerations

The goal of this article is to describe the broad curricular constructs surrounding teaching and learning about social media in dental education. This analysis takes into account timing, development, and assessment of the knowledge, skills, attitudes, and behaviors needed to effectively use social media tools as a contemporary dentist. Three developmental stages in a student’s path to becoming a competent professional are described: from undergraduate to dental student, from the classroom and preclinical simulation laboratory to the clinical setting, and from dental student to licensed practitioner. Considerations for developing the dental curriculum and suggestions for effective instruction at each stage are offered. In all three stages in the future dentist’s evolution, faculty members need to educate students about appropriate professional uses of social media. Faculty members should provide instruction on the beneficial aspects of this communication medium and help students recognize the potential pitfalls associated with its use. The authors provide guidelines for customizing instruction to complement each stage of development, recognizing that careful timing is not only important for optimal learning but can prevent inappropriate use of social media as students are introduced to novel situations

Small businesses in the new creative industries:innovation as a people management challenge

Purpose - This paper presents findings from an SME case study situated in the computer games industry, the youngest and fastest growing of the new digital industries. The study examines changing people management practices as the case company undergoes industry-typical strategic change to embark on explorative innovation and argues that maintaining an organisational context conducive to innovatin over time risks turning into a contest between management and employees as both parties interpret organisational pressures from their different perspectives. Design/methodology/approach - A single case study design is used as the appropriate methdology to generate indepth qualitative data from multiple organisational member perspectives. Findings - Findings indicate that management and worker perspectives on innovation as strategic change and the central people management practices required to support this differ significantly, resulting in tensions and organisational strain. As the company moves to the production of IP work, the need for more effective duality management arises. Research limitations/implications - The single case study has limitations in terms of generalisability. Multiple data collection and triangulation were used to migitate against the limitations. Practical implications - The study highlights the importance of building up change management capability in the small businesses typical for this sector, an as yet neglected focus in the academic iterature concerned with the industry and in support initatives. Originality/value - Few qualitative studies have examined people management practices in the industry in the context of organisational/strategic change, and few have adopted a process perspective

Many-agent Reinforcement Learning

Multi-agent reinforcement learning (RL) solves the problem of how each agent should behave optimally in a stochastic environment in which multiple agents are learning simultaneously. It is an interdisciplinary domain with a long history that lies in the joint area of psychology, control theory, game theory, reinforcement learning, and deep learning. Following the remarkable success of the AlphaGO series in single-agent RL, 2019 was a booming year that witnessed significant advances in multi-agent RL techniques; impressive breakthroughs have been made on developing AIs that outperform humans on many challenging tasks, especially multi-player video games. Nonetheless, one of the key challenges of multi-agent RL techniques is the scalability; it is still non-trivial to design efficient learning algorithms that can solve tasks including far more than two agents ($N \gg 2$), which I name by \emph{many-agent reinforcement learning} (MARL\footnote{I use the world of ``MARL" to denote multi-agent reinforcement learning with a particular focus on the cases of many agents; otherwise, it is denoted as ``Multi-Agent RL" by default.}) problems. In this thesis, I contribute to tackling MARL problems from four aspects. Firstly, I offer a self-contained overview of multi-agent RL techniques from a game-theoretical perspective. This overview fills the research gap that most of the existing work either fails to cover the recent advances since 2010 or does not pay adequate attention to game theory, which I believe is the cornerstone to solving many-agent learning problems. Secondly, I develop a tractable policy evaluation algorithm -- $\alpha^\alpha$-Rank -- in many-agent systems. The critical advantage of $\alpha^\alpha$-Rank is that it can compute the solution concept of $\alpha$-Rank tractably in multi-player general-sum games with no need to store the entire pay-off matrix. This is in contrast to classic solution concepts such as Nash equilibrium which is known to be $PPAD$-hard in even two-player cases. $\alpha^\alpha$-Rank allows us, for the first time, to practically conduct large-scale multi-agent evaluations. Thirdly, I introduce a scalable policy learning algorithm -- mean-field MARL -- in many-agent systems. The mean-field MARL method takes advantage of the mean-field approximation from physics, and it is the first provably convergent algorithm that tries to break the curse of dimensionality for MARL tasks. With the proposed algorithm, I report the first result of solving the Ising model and multi-agent battle games through a MARL approach. Fourthly, I investigate the many-agent learning problem in open-ended meta-games (i.e., the game of a game in the policy space). Specifically, I focus on modelling the behavioural diversity in meta-games, and developing algorithms that guarantee to enlarge diversity during training. The proposed metric based on determinantal point processes serves as the first mathematically rigorous definition for diversity. Importantly, the diversity-aware learning algorithms beat the existing state-of-the-art game solvers in terms of exploitability by a large margin. On top of the algorithmic developments, I also contribute two real-world applications of MARL techniques. Specifically, I demonstrate the great potential of applying MARL to study the emergent population dynamics in nature, and model diverse and realistic interactions in autonomous driving. Both applications embody the prospect that MARL techniques could achieve huge impacts in the real physical world, outside of purely video games

Vol. 8, issue 2

kanopy streaming videos NEW magazines Stereographs Found Welcome to Bryant, Class of 2020 MAKE Fun & Games New Study Room Reservation System E-books from Ebrary Science Titles Free Ear Plugs! When it just can\u27t be TOO quiet... Bryant Land - Aerial View 1939 Open Educational Resources Banned Book Week 2016 Information Literacy at Bryan