How can we test AI safety with resource sharing?
FROM HOMEOSTASIS TO RESOURCE SHARING: BIOLOGICALLY AND ECONOMICALLY COMPATIBLE MULTI-OBJECTIVE MULTI-AGENT AI SAFETY BENCHMARKS
This paper introduces new benchmark environments for testing the safety of multi-agent AI systems, particularly focusing on aspects relevant to biological and economic systems, like homeostasis, resource management, and cooperation. These benchmarks use a grid-world setup, similar to AI Safety Gridworlds but extended to support multiple agents, objectives, and more complex scoring dynamics.
For developers working on LLM-based multi-agent systems, the key takeaway is the introduction of these novel, complex scenarios that go beyond simple reward maximization. These scenarios challenge agents to balance competing objectives, consider long-term sustainability, and cooperate with other agents, all crucial for safe and aligned AI behavior in real-world applications. The paper provides a foundation for rigorously evaluating the safety and performance of LLMs within multi-agent systems, particularly regarding unintended consequences and value alignment.