Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making

2025-10-18

AI Summary

Existing imitation learning methods fail to capture the heterogeneous decision-making behaviors of pollinators—such as honeybees—that rely on memory, numerical cues, and dynamic weather conditions. Specifically, they cannot model adaptive memory window lengths, fast/slow learning under suboptimal strategies, or provide biological interpretability. To address these limitations, we propose a sequential reinforcement learning framework that integrates trajectory similarity analysis with interpretable modeling to automatically identify effective memory windows and establish a theoretical link between bee foraging strategies and multi-armed bandit problems. Our approach innovatively unifies imitation learning, meta-bandit modeling, and environment-aware mechanisms. Experiments successfully reproduce key behavioral patterns, and we release the first high-resolution behavioral trajectory dataset—comprising 80 honeybees across variable weather conditions—enabling unprecedented biological insight into pollinator learning–memory interactions.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

We introduce a sequential reinforcement learning framework for imitation learning designed to model heterogeneous cognitive strategies in pollinators. Focusing on honeybees, our approach leverages trajectory similarity to capture and forecast behavior across individuals that rely on distinct strategies: some exploiting numerical cues, others drawing on memory, or being influenced by environmental factors such as weather. Through empirical evaluation, we show that state-of-the-art imitation learning methods often fail in this setting: when expert policies shift across memory windows or deviate from optimality, these models overlook both fast and slow learning behaviors and cannot faithfully reproduce key decision patterns. Moreover, they offer limited interpretability, hindering biological insight. Our contribution addresses these challenges by (i) introducing a model that minimizes predictive loss while identifying the effective memory horizon most consistent with behavioral data, and (ii) ensuring full interpretability to enable biologists to analyze underlying decision-making strategies and finally (iii) providing a mathematical framework linking bee policy search with bandit formulations under varying exploration-exploitation dynamics, and releasing a novel dataset of 80 tracked bees observed under diverse weather conditions. This benchmark facilitates research on pollinator cognition and supports ecological governance by improving simulations of insect behavior in agroecosystems. Our findings shed new light on the learning strategies and memory interplay shaping pollinator decision-making.

Problem

• Modeling heterogeneous cognitive strategies in pollinator decision-making
• Addressing imitation learning failures with shifting expert policies
• Providing interpretable framework linking bee behavior with bandit formulations

Innovation

• Sequential reinforcement learning for bee imitation
• Model minimizes loss with interpretable memory horizon
• Mathematical framework links bee policies to bandits