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Extended Behavior Networks and Agent Personality: Investigating the Design of Character Stereotypes in the Game Unreal Tournament

  • Hugo da Silva Corrêa Pinto
  • Luis Otávio Alvares
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3661)

Abstract

The Extended Behavior Network (EBN) is an architecture and action selection mechanism to design agents capable of selecting sets of concurrent actions in dynamic and continuous environments. It allows one to specify context-dependent motivations and build agents modularly, and has achieved good results in the Robocup and in the 3D action game Unreal Tournament. PHISH-Nets, another behavior network model capable of selecting just single actions, was applied to character modeling, with promising results. We investigate how EBNs fare on agent personality modeling via the design and analysis of 5 stereotypes in Unreal Tournament. We discuss three ways to build character personas and situate our work within other approaches. We conclude that EBNs provide a straightforward way to develop and experiment with different personalities, being interesting for building agents with simple personas and for character prototyping.

Keywords

Agent Personality Global Parameter Activation Spreading Behavior Network Global Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hugo da Silva Corrêa Pinto
    • 1
  • Luis Otávio Alvares
    • 1
  1. 1.Instituto de InformáticaUniversidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrazil

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