Logical Robots: Declarative Multi-Agent Programming in Logica

Evgeny Skvortsov; Yilin Xia; Ojaswa Garg; Shawn Bowers; Bertram Ludäscher

cs.MAApr 8, 2026

Logical Robots: Declarative Multi-Agent Programming in Logica

Authors: Evgeny Skvortsov, Yilin Xia, Ojaswa Garg, Shawn Bowers, Bertram Ludäscher

Organizations: Google LLC, Kirkland, WA, USA · University of Illinois Urbana-Champaign, IL, USA · Gonzaga University, Spokane, WA, USA

Abstract

We present Logical Robots, an interactive multi-agent simulation platform where autonomous robot behavior is specified declaratively in the logic programming language Logica. Robot behavior is defined by logical predicates that map observations from simulated radar arrays and shared memory to desired motor outputs. This approach allows low-level reactive control and high-level planning to coexist within a single programming environment, providing a coherent framework for exploring multi-agent robot behavior.

Figures & tables

Figure 1. Multi-robot exploration with sensor rays (red endpoints: wall, green endpoints: beacon) and dynamic area toggling triggered by specific beacon detection (e.g., Fire Station).

Figure 2. Distributed mapping scenario: robots collaboratively explore a beacon-filled labyrinth, building a shared navigation graph to find their way “Home.”

Multi-Agent Systems

Multi-Agent Systems studies how autonomous agents coordinate, compete, communicate, and learn in shared or interdependent tasks.

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Oct 12, 2026J/K move · Enter open · S save

Declarative Reactive Robot Behaviors from Imperative Language Models

Vincent Berenz, Tasha Kim, Bernhard Schölkopf

Max Planck Institute for Intelligent Systems, Tübingen, Germany · University of Oxford, Oxford, United Kingdom · ELLIS Institute Tübingen, Germany+1

LLM-Based Program Synthesis AI Coding Agents Language-Conditioned Robot Control

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Large Language Models (LLMs) are predominantly trained on imperative programming languages, and existing work on LLM-based robot programming reflects this: it focuses on goal-directed tasks expressed as plans. However, many robotic applications require not tasks but behaviors--open-ended, continuous patterns of sensory-motor engagement with no termination condition (e.g., greeting visitors, patrolling an area). Such behaviors are better served by declarative and reactive programming, on which LLMs have virtually no training. We investigate whether this gap can be bridged. Using Playful, a reactive scripting language, we show that an LLM-based coding agent can generate correct declarative and reactive programs for robotic behaviors. These programs are produced from under-specified natural language descriptions, given suitable context and a set of reusable programming guidelines. The guidelines encode design principles that redirect the agent from imperative defaults toward proper reactive patterns. Developed once by an expert, they generalize across robots and behaviors. Starting from only the generic robot operating system API, the agent jointly designs both the imperative component (Python modules for sensing, acting, and evaluating) and the declarative component (the reactive script that composes them into behavior logic). The declarative nature of reactive programming supports both incremental refinement through conversational turns and one-shot generation of complete behaviors.

Abstract

Multi-Agent Systems

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