Fig: Automated Multiagent Behavior Elicitor

Key points

• Many social evaluations for multiagent systems study behaviors as they emerge from environments instead of systematically intervening in the environment to elicit such behaviors.1
• It takes a natural language behavior description, decomposes it into sub-behaviors, generates new social games or selects existing ones, and executes single or combined interventions to the games' engine, opponents, instructions, and context.1
• Models attempt the target behavior in 45.2% of episodes on average for valid scenarios generated by Fig for six test behaviors -- cooperation, exploitation, deception, covert coordination, betrayal, and sabotage.1
• Fig shows that multiagent environments can serve as programmable test beds for intentional, controllable, and reproducible behavior evaluation.1

Sources (1)

• [1]Fig: Automated Multiagent Behavior Elicitor
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 08:16 PM

  We introduce Fig (Finding Interactions in Games), an agentic system that performs targeted elicitation interventions on game environments.

  Many social evaluations for multiagent systems study behaviors as they emerge from environments instead of systematically intervening in the environment to elicit such behaviors.

Extractive summary: sentences quoted from the sources.