Symbolic Planning and Control Using Game Theory and Grammatical Inference
This work addresses control synthesis for hybrid systems in adversarial settings, offering a formal guarantee but is incremental as it builds on existing methods in game theory and grammatical inference.
The paper tackles the problem of synthesizing control strategies for hybrid dynamical systems in partially unknown adversarial environments by combining game theory, grammatical inference, and discrete abstractions, guaranteeing that system specifications are met under specific conditions including inferable environment models and observed characteristic samples.
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.