ACE-Bench: Agent Configurable Evaluation with Scalable Horizons and Controllable Difficulty under Lightweight Environments

5 Steps

1. 1

   Initiate an ACE-Bench Evaluation: Begin by defining the core parameters for your AI agent evaluation using ACE-Bench, focusing on the agent(s) you wish to assess and the general evaluation goal.

2. 2

   Configure Agent-Specific Scenarios: Utilize ACE-Bench's 'Agent Configurable Evaluation' feature to tailor assessment scenarios. Define specific conditions, environments, and metrics relevant to your agent's capabilities and design objectives.

3. 3

   Set Scalable Task Horizons: Implement 'Scalable Horizons' to adapt evaluation tasks to varying complexities and lengths. Specify the range or specific values for task duration or depth to thoroughly test agent performance under different temporal constraints.

4. 4

   Adjust Controllable Difficulty Levels: Leverage 'Controllable Difficulty' to precisely tune the challenge level of your evaluation tasks. Define difficulty parameters (e.g., number of obstacles, complexity of decision-making, resource scarcity) to create a robust and fair assessment.

5. 5

   Execute in Lightweight Environments: Run your configured evaluations within ACE-Bench's 'Lightweight Environments'. This ensures reduced computational and time costs, allowing for faster iteration and more efficient benchmarking cycles.