A Deep Learning Agent for Games with Hidden Information

The goal of this project is to develop an agent capable of playing a particular game at an above average human level. In order to do so we investigated reinforcement and deep learning techniques for making decisions in discrete action spaces with hidden information. The methods we used to accomplish this goal include a standard word2vec implementation, an alpha-beta minimax tree search, and an LSTM network to evaluate game states. Given just the rules of the game and a vector representation of the game states, the agent learned to play the game by competitive self play. The emergent behavior from these techniques was compared to human play

Giving voice to equitable collaboration in participatory design

An AHRC funded research project titled Experimenting with the Co-experience Environment (June 2005 – June 2006) culminated in a physical environment designed in resonance with a small group of participants. The participants emerged from different disciplines coming together as a group to share their expertise and contribute their knowledge to design. They engaged in storytelling, individual and co-thinking, creating and co-creating, sharing ideas that did not require justification, proposed designs even though most were not designers …and played. The research questioned how a physical environment designed specifically for co-experiencing might contribute to new knowledge in design? Through play and by working in action together the participants demonstrated the potential of a physical co-experience environment to function as a scaffold for inter-disciplinary design thinking,saying, doing and making (Ivey & Sanders 2006). Ultimately the research questioned how this outcome might influence our approach to engaging participants in design research and experimentation