Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Gaming Business Communities: Developing online learning organisations to foster communities, develop leadership, and grow interpersonal education

This paper explores, through observation and testing, what possibilities from gaming can be extended into other realms of human interaction to help bring people together, extend education, and grow business. It uses through action learning within the safety of the virtual world within Massively Multiplayer Online Games. Further, I explore how the world of online gaming provides opportunity to train a wide range of skills through extending Revans’ (1980) learning equation and action inquiry methodology. This equation and methodology are deployed in relation to a gaming community to see if the theories could produce strong relationships within organisations and examine what learning, if any, is achievable. I also investigate the potential for changes in business (e.g., employee and customer relationships) through involvement in the gaming community as a unique place to implement action learning. The thesis also asks the following questions on a range of extended possibilities in the world of online gaming: What if the world opened up to a social environment where people could discuss their successes and failures? What if people could take a real world issue and re‐create it in the safe virtual world to test ways of dealing with it? What education answers can the world of online gaming provide

Quo vadimus? The 21st Century and multimedia

The concept is related of computer driven multimedia to the NASA Scientific and Technical Information Program (STIP). Multimedia is defined here as computer integration and output of text, animation, audio, video, and graphics. Multimedia is the stage of computer based information that allows access to experience. The concepts are also drawn in of hypermedia, intermedia, interactive multimedia, hypertext, imaging, cyberspace, and virtual reality. Examples of these technology developments are given for NASA, private industry, and academia. Examples of concurrent technology developments and implementations are given to show how these technologies, along with multimedia, have put us at the threshold of the 21st century. The STI Program sees multimedia as an opportunity for revolutionizing the way STI is managed

Digital communities: context for leading learning into the future?

In 2011, a robust, on-campus, three-element Community of Practice model consisting of growing community, sharing of practice and building domain knowledge was piloted in a digital learning environment. An interim evaluation of the pilot study revealed that the three-element framework, when used in a digital environment, required a fourth element. This element, which appears to happen incidentally in the face-to-face context, is that of reflecting, reporting and revising. This paper outlines the extension of the pilot study to the national tertiary education context in order to explore the implications for the design, leadership roles, and selection of appropriate technologies to support and sustain digital communities using the four-element model

Towards a data-driven approach to scenario generation for serious games

Serious games have recently shown great potential to be adopted in many applications, such as training and education. However, one critical challenge in developing serious games is the authoring of a large set of scenarios for different training objectives. In this paper, we propose a data-driven approach to automatically generate scenarios for serious games. Compared with other scenario generation methods, our approach leverages on the simulated player performance data to construct the scenario evaluation function for scenario generation. To collect the player performance data, an artificial intelligence (AI) player model is designed to imitate how a human player behaves when playing scenarios. The AI players are used to replace human players for data collection. The experiment results show that our data-driven approach provides good prediction accuracy on scenario’s training intensities. It also outperforms our previous heuristic-based approach in its capability of generating scenarios that match closer to specified target player performance

Game Changer: Investing in Digital Play to Advance Children's Learning and Health

Based on a literature review and interviews with digital learning experts, explores how digital games can foster skills and knowledge for better academic performance and health. Makes recommendations for government research, partnerships, and media

An integrated approach to rotorcraft human factors research

As the potential of civil and military helicopters has increased, more complex and demanding missions in increasingly hostile environments have been required. Users, designers, and manufacturers have an urgent need for information about human behavior and function to create systems that take advantage of human capabilities, without overloading them. Because there is a large gap between what is known about human behavior and the information needed to predict pilot workload and performance in the complex missions projected for pilots of advanced helicopters, Army and NASA scientists are actively engaged in Human Factors Research at Ames. The research ranges from laboratory experiments to computational modeling, simulation evaluation, and inflight testing. Information obtained in highly controlled but simpler environments generates predictions which can be tested in more realistic situations. These results are used, in turn, to refine theoretical models, provide the focus for subsequent research, and ensure operational relevance, while maintaining predictive advantages. The advantages and disadvantages of each type of research are described along with examples of experimental results