BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems
This addresses the challenge of sample-efficient exploration for AI-driven discovery tasks, offering a practical solution for domains like clean energy materials, though it is incremental as it builds on Bayesian optimization and novelty search concepts.
The paper tackles the problem of efficiently discovering diverse behaviors in expensive black-box systems, such as material design, by introducing BEACON, a Bayesian optimization-based novelty search method that outperforms existing baselines under tight evaluation budgets.
Novelty search (NS) refers to a class of exploration algorithms that seek to uncover diverse system behaviors through simulations or experiments. Such diversity is central to many AI-driven discovery and design tasks, including material and drug development, neural architecture search, and reinforcement learning. However, existing NS methods typically rely on evolutionary strategies and other meta-heuristics that require dense sampling of the input space, making them impractical for expensive black-box systems. In this work, we introduce BEACON, a sample-efficient, Bayesian optimization-inspired approach to NS that is tailored for settings where the input-to-behavior relationship is opaque and costly to evaluate. BEACON models this mapping using multi-output Gaussian processes (MOGPs) and selects new inputs by maximizing a novelty metric computed from posterior samples of the MOGP, effectively balancing the exploration-exploitation trade-off. By leveraging recent advances in posterior sampling and high-dimensional GP modeling, our method remains scalable to large input spaces and datasets. We evaluate BEACON across ten synthetic benchmarks and eight real-world tasks, including the design of diverse materials for clean energy applications. Our results show that BEACON significantly outperforms existing NS baselines, consistently discovering a broader set of behaviors under tight evaluation budgets.