1. Enemy Agent Responding to stimuli in a real time 3D environment

2. The Problem To create a a real-time, AI agent in PC computer game ‘Half-Life’ that can respond to a wide array of stimuli in a fast paced environment and be an enjoyable challenge to fight in the game.

3. Designing the Agent The concept of a game ‘modification’ and its relation to all areas of modern coding practice. Other research in this area. [2] Modelling behaviours on territorial animals, leading to a focus on non-typical computer game behaviours. [5] Designing the ‘fun factor’; immersion and evoking emotions in a human game player.[1]

4. The Plan Basic functionality first style of design.[3] An agent that could respond interestingly to a wide variety of player behaviours rather than just blindly attacking. Putting the intelligence into the environment [4]. Working together with other agents.

5. Enemy Agent Class Diagram Red = Completely original to this project Green = Modified for this project Blue = Standard Half-Life class

6. The Implementation Using a new language; C++ The challenges and advantages of working with someone else’s code. 3D aspect required relearning of mathematics of trigonometry and vectors. Extensive non coding materials required to get project functioning.

7. Agent’s in Action Three key variables control the agents behaviour

8. What the project achieved Created a functioning and hopefully fun in- game opponent. Made the in-game opponent display a variety of interesting responses to player driven stimuli. Successfully overcame implementation difficulties to implement the crucial specification requirements.

9. Evaluation Some fringe aspects of the specification not completed due to difficulty and time constraints. Learned a huge array of new skills and feel the project was well planned and very satisfactorily delivered. Future work could focus on agents developing learning behaviour and reacting to a wider range of emotional drives, increasing the believability of the agents behaviour.

10. Summary Project achieved its key aims. Targeted and background research has achieved excellent understanding of the subject area. Agent was well planned and designed in a very robust way.

11. References [1] Agents that Want and Like: Motivational and Emotional Roots of Cognition and Action - Papers from the AISB’05 Symposium by SSAISB Press [2] It knows What You Are Going To Do: Adding anticipation to a Quakebot – By John E. Laird – Published 2001 http://ai.eecs.umich.edu/people/laird/papers/Agents01.pdf available on 21 st April 2005 [3] Game Architecture and Design: A New Edition - Andrew Rollings and Dave Morris - Published 2003 by New Riders [4] Solving the right problem – Neil Kirby, Bell Laboratories – Published 2002 AI Game Programming Wisdom, Charles River Media [5] Territorial intrusion risk and antipredator behaviour: a mathematical model - Diaz- Uriarte R - Proceedings: Biological Sciences, Volume 268, Number 1472 / June 7, 2001