Partial Observability as a game mechanic
The field of research into Artificial Intelligence (AI) in games has been growing with conferences like the IEEE conference on Computational Intelligence in Games (CIG) and journals like the IEEE Transactions on Computational Intelligence and AI in Games (TCIAIG) leading the charge. Gaming is a huge industry with more and more people regularly playing games for entertainment as well as an increasing number of applications for serious games. All of this is largely driven by the inherent pleasures in playing good and well designed games.
The mainstream game market has stagnated with business decisions reducing risk instead of continually looking to innovate. Games are often endlessly repeated by minor variations as a sequel with new story lines but the same mechanics and methodology. In more modern times, even less innovation is happening with the trend towards creating ”HD” remakes of classic games, improving the graphics but otherwise sticking with the old game design and story.
Of the game mechanics that alter between games, very little interesting work is done with co-operation or Partial Observability (PO). The research that follows aims to tackle some of the challenges faced when varying PO as well as directly researching the effect of PO on game enjoyment and difficulty. PO games are a challenge for creating competent AI. If better AI techniques are found, it may prove less risky to create games with more interesting uses of the PO mechanic.
Piers graduated from the University of Essex with an MSc in Computer Science. His interests lie in the field of Artificial Intelligence and in particular Multi-Agent Systems.
Home institution: Essex
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