DEVELOPING A ROBOTICS OUTREACH PROGRAM

The WPI-EBOT educational robotics program was designed as a low-cost way to teach basic engineering and programming principles and to encourage high school students to pursue an education in engineering or science. The project group recruited local high schools, trained teachers at those schools, and worked directly with students to assist them in building a competitive robot. The schools\u27 response to the project was overwhelmingly positive, and they plan to remain involved for years to come

Innovative Tokyo

This paper compares and contrasts Tokyo's innovation structure with the industrial districts model and the international hub model in the literature on urban and regional development. The Tokyo model embraces and yet transcends both industrial districts and international hub models. The paper details key elements making up the Tokyo model-organizational knowledge creation, integral and co-location systems of corporate R&D and new product development, test markets, industrial districts and clusters, participative consumer culture, continuous learning from abroad, local government policies, the national system of innovation, and the historical genesis of Tokyo in Japan's political economy. The paper finds that the Tokyo model of innovation will continue to evolve with the changing external environment, but fundamentally retains its main characteristics. The lessons from the Tokyo model is that openness, a diversified industrial base, the continuing development of new industries, and an emphasis on innovation, all contribute to the dynamism of a major metropolitan region.Labor Policies,Environmental Economics&Policies,Public Health Promotion,ICT Policy and Strategies,Agricultural Knowledge&Information Systems,ICT Policy and Strategies,Environmental Economics&Policies,Health Monitoring&Evaluation,Agricultural Knowledge&Information Systems,Innovation

Boosting Studies of Multi-Agent Reinforcement Learning on Google Research Football Environment: the Past, Present, and Future

Even though Google Research Football (GRF) was initially benchmarked and studied as a single-agent environment in its original paper, recent years have witnessed an increasing focus on its multi-agent nature by researchers utilizing it as a testbed for Multi-Agent Reinforcement Learning (MARL). However, the absence of standardized environment settings and unified evaluation metrics for multi-agent scenarios hampers the consistent understanding of various studies. Furthermore, the challenging 5-vs-5 and 11-vs-11 full-game scenarios have received limited thorough examination due to their substantial training complexities. To address these gaps, this paper extends the original environment by not only standardizing the environment settings and benchmarking cooperative learning algorithms across different scenarios, including the most challenging full-game scenarios, but also by discussing approaches to enhance football AI from diverse perspectives and introducing related research tools. Specifically, we provide a distributed and asynchronous population-based self-play framework with diverse pre-trained policies for faster training, two football-specific analytical tools for deeper investigation, and an online leaderboard for broader evaluation. The overall expectation of this work is to advance the study of Multi-Agent Reinforcement Learning on Google Research Football environment, with the ultimate goal of benefiting real-world sports beyond virtual games

Simulated Experince Evaluation in Developing Multi-agent Coordination Graphs

Cognitive science has proposed that a way people learn is through self-critiquing by generating \u27what-if\u27 strategies for events (simulation). It is theorized that people use this method to learn something new as well as to learn more quickly. This research adds this concept to a graph-based genetic program. Memories are recorded during fitness assessment and retained in a global memory bank based on the magnitude of change in the agent’s energy and age of the memory. Between generations, candidate agents perform in simulations of the stored memories. Candidates that perform similarly to good memories and differently from bad memories are more likely to be included in the next generation. The simulation-informed genetic program is evaluated in two domains: sequence matching and Robocode. Results indicate the algorithm does not perform equally in all environments. In sequence matching, experiential evaluation fails to perform better than the control. However, in Robocode, the experiential evaluation method initially outperforms the control then stagnates and often regresses. This is likely an indication that the algorithm is over-learning a single solution rather than adapting to the environment and that learning through simulation includes a satisficing component

Database of Video Games and Their Therapeutic Properties

There are reported to be 2.96 billion video game players in the world as of 2021 and this number is expected to grow to 3.32 billion by the year 2024. Of that total, 215.5 million video game players live in the United States with a reported average age of 33 years old. Thousands of commercial video games are released every year. There is evidence to support video game technology use as therapeutic media however it predominately utilizes outdated technology or technology designed for a specific purpose also called “serious games.” The problem is that OT practitioners are unaware of the potential therapeutic properties of video games they have not played, so are unable to integrate unfamiliar video games as therapeutic media in clinical practice. The purpose of this capstone project is to develop an online database of commercial video games, and their therapeutic properties, to facilitate their use as therapeutic media in OT practice. To address this problem a webpage was developed in partnership with the Family Gaming Database that cataloged 10 commercial video games from commercially available video game subscription services and the Nintendo Switch. The 10 games were subject to an activity analysis based on the AMPS to determine their therapeutic potential. The resulting webpage contains three main lists in which filters can be applied in order to display games that meet a specific desired criterion. Applicable filters include platform, age rating, difficulty, and specific accessibility features. Keywords: database, occupational therapy, video game, video game

2012 Annual Report

The 2012 annual report for the University of Bridgeport