Advancing NeSy AI with Abstract Urban Simulation
Recent years have witnessed the rapid development of Neuro-Symbolic (NeSy) AI systems, which integrate symbolic reasoning into deep neural networks. However, most existing benchmarks for NeSy AI fail to provide long-horizon reasoning task with complex multi-agent interaction. Furthermore, they are usually constrained by fixed and simplistic logical rules over limited entities, making them inadequate for capturing 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 governing 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 benefit of our LogiCity is its support for user-configurable abstractions, enabling customizable simulation complexities for logical reasoning.
To explore various aspects of NeSy AI, we 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 using LogiCity 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 reasoning scenarios or under high-dimensional, imbalanced data. With the flexible design, various features, and newly raised challenges, we believe LogiCity represents a pivotal step for advancing the next generation of NeSy AI.
