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Enemy Agent

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

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Enemy Agent

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  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 SymposiumbySSAISB Press [2] It knows What You Are Going To Do: Adding anticipation to a Quakebot – ByJohn E. Laird – Published 2001 http://ai.eecs.umich.edu/people/laird/papers/Agents01.pdf available on 21st April 2005 [3] Game Architecture and Design: A New Edition - Andrew Rollings and Dave Morris- Published2003 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

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