Research

My research works in the interface between artificial intelligence and game design. I am interested in the use of AI tools to give more power to designers to create richer player experiences in games. One of the critical limitations of current computer games is our inability to make rich emergence models of social systems. As a result, social interaction in games is obvious and clunky; it lacks the subtlety, playfulness and room for mastery that we can provide in physical systems.

My work is based around a theme of abstraction. Abstraction is the process of finding useful high-level chunks that enable us to talk and reason about complex concrete systems without having to go into complete detail. In AI abstraction can be used as a divide-and-conquer approach, allowing us to factor a problem into independent parts and solve them individually. In education, abstraction is our fundamental tool for learning. We take concrete experiences and use them to build abstract mental models with which we understand the world. In game design, abstraction is the key force behind emergent gameplay.

Active research projects:

Past research projects:


Game design and analysis

Game design is still a very young discipline and has little established knowledge. Practice far outstrips theory. We need to analyse games and reflect on the processes we use for design to better understand how we do the things we do.

Selected papers:


Emergence and Games-based learning

Experiential learning theory tells us we learn by constructing abstract mental models of the concrete world through reflection and observation and test these models by active experimentation. Academic learning often fails to make this connected cycle between concrete experience and abstract knowledge. Games provide an opportunity to learn about systems through hands-on experience, but only if the game is designed to support playful discovery and mastery.

Selected papers:


Character Modelling for Narrative Generation

Computer games are much better at simulating physical systems than social systems. As a result, it is much easier to make a playful physics game than a playful social or story-based game. Story is very difficult to model, it involves understanding the world in terms of action, character and plot. The same action can have several different ’causes’ in a game:

  • The World: The state of the world that precipitated the action.
  • The Character: The intent of the character who performed the action.
  • The Author: The intent of the author who wrote the action.

Selected papers:


Multiagent path planning

Planning paths for multiple agents (robots, game characters, network packets) to simultaneous move around a shared space without colliding is a computationally difficult problem. However real-world maps have structure that we can exploit to make the problem much easier.

Selected papers:


Hierarchical reinforcement learning

My PhD research investigated the synthesis of semi-Markov reinforcement learning with teleo-reactive planning. I built a system, called Rachel, which could construct an abstract plan based on user-specified teleo-operators (TOPs) and then learn a concrete implementation of that plan using reinforcement learning.

Selected papers:

Published on December 13, 2012 at 5:23 am  Leave a Comment  

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