{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/probabilistic-model-checking-for-complex","title":"Probabilistic Model Checking for Complex Cognitive Tasks -- A case study in human-robot interaction","arxiv_id":"1610.09409","date":"2016-10-28","proceeding":null,"authors":["Sebastian Junges","Nils Jansen","Joost-Pieter Katoen","Ufuk Topcu"],"abstract":"This paper proposes to use probabilistic model checking to synthesize optimal\nrobot policies in multi-tasking autonomous systems that are subject to\nhuman-robot interaction. Given the convincing empirical evidence that human\nbehavior can be related to reinforcement models, we take as input a\nwell-studied Q-table model of the human behavior for flexible scenarios. We\nfirst describe an automated procedure to distill a Markov decision process\n(MDP) for the human in an arbitrary but fixed scenario. The distinctive issue\nis that -- in contrast to existing models -- under-specification of the human\nbehavior is included. Probabilistic model checking is used to predict the\nhuman's behavior. Finally, the MDP model is extended with a robot model.\nOptimal robot policies are synthesized by analyzing the resulting two-player\nstochastic game. Experimental results with a prototypical implementation using\nPRISM show promising results.","url_abs":"http://arxiv.org/abs/1610.09409v1","url_pdf":"http://arxiv.org/pdf/1610.09409v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"probabilistic-model-checking-for-complex","repo_url":"https://github.com/moves-rwth/human_factor_models","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}