DRCO: a Toolkit for Intelligently Curbing Illegal Wildlife Trade

Although generative AI has been applied to protect wildlife in various scenarios, studies have identified the lack of an integrated application toolkit to curb illegal wildlife trade. We therefore introduce DRCO1 , the first application framework that is integrated with LLM by proposing useful policies. In the Decision-Making Module, a black-box LLM, combined with a refined ReAct-like prompting template, selects policy promoters in one region. In the Restrictive-Partial-Legalization Module, our innovative Dynamic Iterative Constraint Method is employed to calculate the controlled volume of wildlife trade under the influence of policy, which may provide an idea for Explainable AI research. The Curve-fitting module fits current and future data into a curve with an 86.27% fit to the original Product Life Cycle Curve. The Optimal-Algorithm Module proposes a Dynamic WP-CUCB Algorithm with Policy Consideration to optimize the allocation of wildlife patrol resources in the region. Experiments indicate that policies generated by DRCO can lead to significant control and promote “AI for social good”. Our code and more details will be open-sourced online.