Symbolic Planning and Control Using Game Theory and Grammatical Inference

TL;DR

This paper introduces a novel approach combining game theory, grammatical inference, and discrete abstractions to synthesize control strategies for hybrid systems in uncertain, adversarial environments, ensuring task satisfaction under certain conditions.

Contribution

It integrates game theory with grammatical inference and discrete abstractions to enable control synthesis in partially unknown adversarial environments, providing formal guarantees.

Findings

Guarantees task satisfaction if environment model is inferable from observations.

Ensures control strategies are valid given the system's capabilities.

Applicable to hybrid dynamical systems in rule-governed environments.

Abstract

This paper presents an approach that brings together game theory with grammatical inference and discrete abstractions in order to synthesize control strategies for hybrid dynamical systems performing tasks in partially unknown but rule-governed adversarial environments. The combined formulation guarantees that a system specification is met if (a) the true model of the environment is in the class of models inferable from a positive presentation, (b) a characteristic sample is observed, and (c) the task specification is satisfiable given the capabilities of the system (agent) and the environment.