Towards Unified Alignment Between Agents, Humans, and Environment
CoRR(2024)
摘要
The rapid progress of foundation models has led to the prosperity of
autonomous agents, which leverage the universal capabilities of foundation
models to conduct reasoning, decision-making, and environmental interaction.
However, the efficacy of agents remains limited when operating in intricate,
realistic environments. In this work, we introduce the principles of
𝐔nified 𝐀lignment for 𝐀gents
(𝐔𝐀^2), which advocate for the simultaneous alignment of agents with
human intentions, environmental dynamics, and self-constraints such as the
limitation of monetary budgets. From the perspective of 𝐔𝐀^2, we
review the current agent research and highlight the neglected factors in
existing agent benchmarks and method candidates. We also conduct
proof-of-concept studies by introducing realistic features to WebShop,
including user profiles to demonstrate intentions, personalized reranking for
complex environmental dynamics, and runtime cost statistics to reflect
self-constraints. We then follow the principles of 𝐔𝐀^2 to propose
an initial design of our agent, and benchmark its performance with several
candidate baselines in the retrofitted WebShop. The extensive experimental
results further prove the importance of the principles of 𝐔𝐀^2. Our
research sheds light on the next steps of autonomous agent research with
improved general problem-solving abilities.
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