Agents
Can an AI Agent Safely Run a Government Existence of Probably Approximately Aligned Policies
Y et, to our knowledge, every metric for alignment is a posteriori, i.e., a system is deemed aligned as
e197fe307eb3467035f892dc100d570a-Supplemental-Conference.pdf
The process for calculating these metrics is described in Appendix C. Moreover, to ensure the comparability between prediction performance metrics and driving performance metrics in the radar plot, we normalize all metrics to the scale of [0, 1]. In the subsequent section, we provide an overview of the DESPOT planner. These two values can only be inferred from history. The safety is represented by the normalized collision rate.
Few-Shot Task Learning through Inverse Generative Modeling
Learning the intents of an agent, defined by its goals or motion style, is often extremely challenging from just a few examples.
TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration
Unfortunately, these models are "isolated agents" with heterogeneous
StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving Chang Gao
It employs four LLM-based agents: strategy generator, executor, optimizer, and evaluator, working together to generate, evaluate, and select promising strategies for a given task. Experimental results demonstrate that StrategyLLM outperforms the competitive baseline CoT -SC that requires human-annotated solutions on 13 datasets across 4 challenging tasks without human involvement, including math reasoning (34.2%
