Interpretable apprenticeship learning with temporal logic specifications

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@InProceedings{Kasenberg:2017:ieeeCDC,

• author = "D. Kasenberg and M. Scheutz",
• booktitle = "2017 IEEE 56th Annual Conference on Decision and Control (CDC)",
• title = "Interpretable apprenticeship learning with temporal logic specifications",
• year = "2017",
• pages = "4914--4921",
• abstract = "Recent work has addressed using formulas in linear temporal logic (LTL) as specifications for agents planning in Markov Decision Processes (MDPs). We consider the inverse problem: inferring an LTL specification from demonstrated behaviour trajectories in MDPs. We formulate this as a multiobjective optimisation problem, and describe state-based (what actually happened) and action-based (what the agent expected to happen) objective functions based on a notion of violation cost. We demonstrate the efficacy of the approach by employing genetic programming to solve this problem in two simple domains.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CDC.2017.8264386",
• month = dec,
• notes = "Also known as \cite{8264386}",

}

Genetic Programming entries for D Kasenberg M Scheutz

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