Attractors in recurrent behavior networks
Attractors in recurrent behavior networks
Human conversation as a system framework: designing embodied conversational agents
Embodied conversational agents
Beyond the Plan-Length Criterion
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
AI characters and directors for interactive computer games
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
A layered brain architecture for synthetic creatures
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Creating adaptive and individual personalities in many characters without hand-crafting behaviors
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
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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.