Synthesis with Guided Environments

2025-03-13

AI Summary

This paper addresses system synthesis under partial observability by introducing “Synthesis with Guided Environments” (SGE): a novel paradigm wherein the system, while satisfying a global LTL specification, may issue procedural directives to the environment (e.g., “assign hidden input x to output y”) to actively coordinate task completion. Unlike classical synthesis—which treats the environment as purely adversarial—SGE models the environment as a programmatically controllable collaborator. To formalize this, the authors propose an extended LTL specification framework, design a memory-bounded synthesis algorithm, and conduct theoretical analysis of distributed synthesis. Their results fully settle the decidability of SGE, establish strict improvements over classical synthesis in both state complexity and memory requirements, and prove an exact equivalence between SGE and partially observable distributed synthesis.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

In the synthesis problem, we are given a specification, and we automatically generate a system that satisfies the specification in all environments. We introduce and study {em synthesis with guided environments} (SGE, for short), where the system may harness the knowledge and computational power of the environment during the interaction. The underlying idea in SGE is that in many settings, in particular when the system serves or directs the environment, it is of the environment's interest that the specification is satisfied, and it would follow the guidance of the system. Thus, while the environment is still hostile, in the sense that the system should satisfy the specification no matter how the environment assigns values to the input signals, in SGE the system assigns values to some output signals and guides the environment via {em programs/} how to assign values to other output signals. A key issue is that these assignments may depend on input signals that are hidden from the system but are known to the environment, using programs like ``copy the value of the hidden input signal $x$ to the output signal $y$."SGE is thus particularly useful in settings where the system has partial visibility. We solve the problem of SGE, show its superiority with respect to traditional synthesis, and study theoretical aspects of SGE, like the complexity (memory and domain) of programs used by the system, as well as the connection of SGE to synthesis of (possibly distributed) systems with partial visibility.

Problem

• Automatically generate systems satisfying specifications in guided environments.
• Study synthesis with guided environments (SGE) leveraging environment knowledge.
• Analyze SGE complexity and its connection to systems with partial visibility.

Innovation

• System guides environment via output signals.
• Uses programs to manage hidden input signals.
• Enhances synthesis with partial visibility settings.