用可定制逻辑模拟城市，推动神经符号AI长时多智能体推理
LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation
- 基于一阶逻辑构建可配置的城市模拟器，支持动态多主体交互
- 在长序列决策任务中验证神经符号模型优于传统方法
- 适合研究复杂抽象推理与多智能体系统的研究人员
近年来，神经符号（NeSy）AI系统快速发展，将符号推理融入深度神经网络。然而，现有基准大多缺乏长期推理任务和复杂的多智能体互动，且受限于固定简化的逻辑规则和有限实体，难以反映真实世界复杂性。为此，我们提出LogiCity，首个基于可定制一阶逻辑（FOL）的城市类环境模拟器，包含多个动态智能体。通过语义与空间概念（如IsAmbulance(X)、IsClose(X,Y)）建模多样化城市元素，并定义FOL规则控制智能体行为。由于概念与规则为抽象表达，可通用适配任意组成的城市场景，实现多样情景的快速构建。此外，LogiCity支持用户自定义抽象层级，灵活调节推理复杂度。为探索NeSy AI多方面能力，引入两项任务：一项聚焦长时序决策，另一项关注单步视觉推理，难度与智能体行为各异。大量实验表明，神经符号框架在抽象推理上具有显著优势；同时揭示了在长期多智能体场景或高维不平衡数据下处理复杂抽象的严峻挑战。凭借其灵活设计、丰富功能及新提出的问题，我们认为LogiCity是推动下一代NeSy AI发展的关键一步。代码与数据已开源。
原文摘要 · Abstract (English)
Recent years have witnessed the rapid development of Neuro-Symbolic (NeSy) AI systems, which integrate symbolic reasoning into deep neural networks. However, most of the existing benchmarks for NeSy AI fail to provide long-horizon reasoning tasks with complex multi-agent interactions. Furthermore, they are usually constrained by fixed and simplistic logical rules over limited entities, making them far from real-world complexities. To address these crucial gaps, we introduce LogiCity, the first simulator based on customizable first-order logic (FOL) for an urban-like environment with multiple dynamic agents. LogiCity models diverse urban elements using semantic and spatial concepts, such as IsAmbulance(X) and IsClose(X, Y). These concepts are used to define FOL rules that govern the behavior of various agents. Since the concepts and rules are abstractions, they can be universally applied to cities with any agent compositions, facilitating the instantiation of diverse scenarios. Besides, a key feature of LogiCity is its support for user-configurable abstractions, enabling customizable simulation complexities for logical reasoning. To explore various aspects of NeSy AI, LogiCity introduces two tasks, one features long-horizon sequential decision-making, and the other focuses on one-step visual reasoning, varying in difficulty and agent behaviors. Our extensive evaluation reveals the advantage of NeSy frameworks in abstract reasoning. Moreover, we highlight the significant challenges of handling more complex abstractions in long-horizon multi-agent scenarios or under high-dimensional, imbalanced data. With its flexible design, various features, and newly raised challenges, we believe LogiCity represents a pivotal step forward in advancing the next generation of NeSy AI. All the code and data are open-sourced at our website: https://jaraxxus-me.github.io/LogiCity/
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