DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents (2406.06769v2)

Abstract: Automated scientific discovery promises to accelerate progress across scientific domains. However, developing and evaluating an AI agent's capacity for end-to-end scientific reasoning is challenging as running real-world experiments is often prohibitively expensive or infeasible. In this work we introduce DISCOVERYWORLD, the first virtual environment for developing and benchmarking an agent's ability to perform complete cycles of novel scientific discovery. DISCOVERYWORLD contains a variety of different challenges, covering topics as diverse as radioisotope dating, rocket science, and proteomics, to encourage development of general discovery skills rather than task-specific solutions. DISCOVERYWORLD itself is an inexpensive, simulated, text-based environment (with optional 2D visual overlay). It includes 120 different challenge tasks, spanning eight topics each with three levels of difficulty and several parametric variations. Each task requires an agent to form hypotheses, design and run experiments, analyze results, and act on conclusions. DISCOVERYWORLD further provides three automatic metrics for evaluating performance, based on (a) task completion, (b) task-relevant actions taken, and (c) the discovered explanatory knowledge. We find that strong baseline agents, that perform well in prior published environments, struggle on most DISCOVERYWORLD tasks, suggesting that DISCOVERYWORLD captures some of the novel challenges of discovery, and thus that DISCOVERYWORLD may help accelerate near-term development and assessment of scientific discovery competency in agents. Code available at: www.github.com/allenai/discoveryworld

DiscoveryWorld: A Virtual Environment for Scientific Discovery Agents

The paper introduces DiscoveryWorld, a significant advancement in the development and assessment of AI agents capable of performing automated scientific discovery. This virtual environment is meticulously designed to simulate the end-to-end process of scientific discovery, aiming to cultivate and evaluate general discovery skills in AI, rather than task-specific solutions.

Overview of DiscoveryWorld

DiscoveryWorld emphasizes a comprehensive, low-cost, simulated environment where AI agents engage in the complete scientific discovery cycle. This includes hypothesis formation, experiment design, data analysis, and the application of conclusions. The environment consists of 120 challenge tasks across eight distinct topics such as proteomics, chemistry, archaeology, and more. Each topic is designed to test different facets of scientific reasoning, promoting the development of general-purpose AI discovery skills.

Key Features and Structure

The system includes various difficulty levels and parametric variations to ensure robust testing across different scenarios. Tasks are not contrived; they incorporate realistic scientific challenges that require both domain-specific and commonsense knowledge. The environment integrates three automatic metrics to evaluate the agent's performance: task completion, task-relevant actions, and discovered explanatory knowledge. These metrics ensure a nuanced assessment of an agent's capabilities.

Comparative Analysis and Baseline Evaluation

The research includes an empirical evaluation using baseline agents such as ReAct, Plan+Execute, and Hypothesizer, highlighting the challenges encountered by current AI capabilities in DiscoveryWorld's tasks. The agents struggle with several tasks, especially those requiring intricate scientific discovery processes, indicating potential areas for future research and development within AI.

Conversely, human participants with varying scientific expertise demonstrated superior performance on many tasks, underscoring the complexity and depth of DiscoveryWorld. The agents' difficulty in completing tasks that were manageable for human scientists signifies critical gaps in current AI technology regarding end-to-end scientific reasoning.

Implications and Future Directions

DiscoveryWorld represents a strategic step toward enhancing AI's scientific discovery capabilities. The environment's realistic simulation of scientific processes offers a foundational benchmark for future AI models. By fostering general discovery skills, DiscoveryWorld sets the stage for AI systems that can contribute meaningfully across broad scientific domains.

The challenges experienced by baseline agents suggest areas for algorithmic improvements, particularly in the realms of scientific reasoning and knowledge integration. Future research could explore advanced techniques for improving AI’s hypothesis generation, experimentation, and data analysis skills.

Conclusion

DiscoveryWorld is a pioneering development in AI research, providing a critical platform for testing and refining scientific discovery agents. The environment's design and evaluation framework offer insights into the current limitations and future potential of AI in performing complex, multidisciplinary research processes. As researchers leverage DiscoveryWorld, it is poised to drive innovations that could significantly accelerate scientific progress.