Creative communities:shaping process through performance and play

This paper studies the use of play as a method to unlock creativity and innovation within a community of practice (a group of individuals who share a common interest and who see value in interaction to enhance their understanding). An analysis of communities of practice and the value of play informs evaluation of two case studies exploring the development of communities of practice, one within the discipline of videogames and one which bridges performing arts and videogames. The case studies provide qualitative data from which the potential of play, as a method to inspire creativity and support the development of a potential community of practice, is recognised. Establishing trust, disruption of process through play and reflection are key steps proposed in a ‘context provider’s framework’ for individuals or organisations to utilise in the design of activities to support creative process and innovation within a potential community of practice

Prediction of intent in robotics and multi-agent systems.

Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches

D7.2 1st experiment planning and community management

The present deliverable, outlines the overall strategy for approaching the tasks of (a) developing and sustaining an engaged school-based community of ProsocialLearn users; and (b)planning and facilitating small-scale and large-scale school-based evaluation studies of the Prosocial Learn technological solution. It also presents the preliminary work undertaken so far, and details the activities planned for M9-15 with respect to community development and small-scale studies

Knowledge Transfer Needs and Methods

INE/AUTC 12.3

Exploiting Opponent Modeling For Learning In Multi-agent Adversarial Games

An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent’s actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players’ physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our approach for both offline play generation and real-time team response in the Rush 2008 American football simulator. Simultaneously predicting the movement trajectories, future reward, and play strategies of multiple players in real-time is a daunting task but we illustrate how it is possible to divide and conquer this problem with an assortment of data-driven models. By leveraging spatio-temporal traces of player movements, we learn discriminative models of defensive play for opponent modeling. With the reward information from previous play matchups, we use a modified version of UCT (Upper Conference Bounds applied to Trees) to create new offensive plays and to learn play repairs to counter predicted opponent actions. iii In team games, players must coordinate effectively to accomplish tasks while foiling their opponents either in a preplanned or emergent manner. An effective team policy must generate the necessary coordination, yet considering all possibilities for creating coordinating subgroups is computationally infeasible. Automatically identifying and preserving the coordination between key subgroups of teammates can make search more productive by pruning policies that disrupt these relationships. We demonstrate that combining opponent modeling with automatic subgroup identification can be used to create team policies with a higher average yardage than either the baseline game or domain-specific heuristics

...something shining, like gold--but better. The National Indian Youth Leadership Model: A Manual for Program Leaders

A lot of people are talking about education these days, including President Bush,who recently launched his America 2000 plan here in St. Paul. It hasn\u27t been like this for many years, certainly not during the 1980s when the issue of young people dropping out, pushed out, or bored out was almost invisible on the national agenda.For McClellan Hall, however, concern for how children learn-especially Indian children, has been a lifelong mission. As national interest focuses on education, it is essential that credible voices such as McClellan\u27s, voices which neither claim nor seek a national limelight, be heard amid the current chorus of those who aggressively press their agendas for children and schools. People who take time to listen to McClellan Hall will not be disappointed

The Cowl - v.79 - n.3 - Sep 18, 2014

The Cowl - student newspaper of Providence College. Vol 79 - No. 3 - September 18, 2014. 24 pages