Diversity in behaviors is instrumental for robust team performance in many multiagent tasks which require agents to coordinate. Unfortunately, exhaustive search through the agents’ behavior spaces is often intractable. This paper introduces Behavior Exploration for Heterogeneous Teams (BEHT), a multi-level learning framework that enables agents to progressively explore regions of the behavior space that promote team coordination on diverse goals. By combining diversity search to maximize agent-specific rewards and evolutionary optimization to maximize the team-based fitness, our method effectively filters regions of the behavior space that are conducive to agent coordination. We demonstrate the diverse behaviors and synergies that are method allows agents to learn on a multiagent exploration problem.
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Coupling agent motivations and spatial behaviors for authoring multiagent narratives
Authoring behavior narratives for heterogeneous multiagent virtual humans engaged in collaborative, localized, and task‐based behaviors can be challenging. Traditional behavior authoring frameworks are either
- NSF-PAR ID:
- 10371938
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Computer Animation and Virtual Worlds
- Volume:
- 30
- Issue:
- 3-4
- ISSN:
- 1546-4261
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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