{"publication_identifier":{"issn":["2664-1690"]},"publication_status":"published","file":[{"date_updated":"2020-07-14T12:46:52Z","date_created":"2018-12-12T11:53:59Z","file_id":"5521","file_size":531046,"relation":"main_file","access_level":"open_access","checksum":"b1d573bc04365625ff9974880c0aa807","file_name":"IST-2014-315-v1+1_report.pdf","creator":"system","content_type":"application/pdf"}],"_id":"5428","year":"2014","language":[{"iso":"eng"}],"day":"05","date_updated":"2023-09-20T12:07:48Z","status":"public","ddc":["004"],"type":"technical_report","alternative_title":["IST Austria Technical Report"],"doi":"10.15479/AT:IST-2014-315-v1-1","department":[{"_id":"ToHe"},{"_id":"KrCh"}],"pubrep_id":"315","file_date_updated":"2020-07-14T12:46:52Z","date_created":"2018-12-12T11:39:16Z","page":"26","oa_version":"Published Version","date_published":"2014-12-05T00:00:00Z","title":"Quantitative fair simulation games","citation":{"ista":"Chatterjee K, Henzinger TA, Otop J, Velner Y. 2014. Quantitative fair simulation games, IST Austria, 26p.","ieee":"K. Chatterjee, T. A. Henzinger, J. Otop, and Y. Velner, <i>Quantitative fair simulation games</i>. IST Austria, 2014.","mla":"Chatterjee, Krishnendu, et al. <i>Quantitative Fair Simulation Games</i>. IST Austria, 2014, doi:<a href=\"https://doi.org/10.15479/AT:IST-2014-315-v1-1\">10.15479/AT:IST-2014-315-v1-1</a>.","apa":"Chatterjee, K., Henzinger, T. A., Otop, J., &#38; Velner, Y. (2014). <i>Quantitative fair simulation games</i>. IST Austria. <a href=\"https://doi.org/10.15479/AT:IST-2014-315-v1-1\">https://doi.org/10.15479/AT:IST-2014-315-v1-1</a>","chicago":"Chatterjee, Krishnendu, Thomas A Henzinger, Jan Otop, and Yaron Velner. <i>Quantitative Fair Simulation Games</i>. IST Austria, 2014. <a href=\"https://doi.org/10.15479/AT:IST-2014-315-v1-1\">https://doi.org/10.15479/AT:IST-2014-315-v1-1</a>.","ama":"Chatterjee K, Henzinger TA, Otop J, Velner Y. <i>Quantitative Fair Simulation Games</i>. IST Austria; 2014. doi:<a href=\"https://doi.org/10.15479/AT:IST-2014-315-v1-1\">10.15479/AT:IST-2014-315-v1-1</a>","short":"K. Chatterjee, T.A. Henzinger, J. Otop, Y. Velner, Quantitative Fair Simulation Games, IST Austria, 2014."},"has_accepted_license":"1","month":"12","oa":1,"author":[{"full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu","last_name":"Chatterjee"},{"orcid":"0000−0002−2985−7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A","last_name":"Henzinger","full_name":"Henzinger, Thomas A"},{"last_name":"Otop","first_name":"Jan","id":"2FC5DA74-F248-11E8-B48F-1D18A9856A87","full_name":"Otop, Jan"},{"first_name":"Yaron","last_name":"Velner","full_name":"Velner, Yaron"}],"publisher":"IST Austria","abstract":[{"text":"Simulation is an attractive alternative for language inclusion for automata as it is an under-approximation of language inclusion, but usually has much lower complexity. For non-deterministic automata, while language inclusion is PSPACE-complete, simulation can be computed in polynomial time. Simulation has also been extended in two orthogonal directions, namely, (1) fair simulation, for simulation over specified set of infinite runs; and (2) quantitative simulation, for simulation between weighted automata. Again, while fair trace inclusion is PSPACE-complete, fair simulation can be computed in polynomial time. For weighted automata, the (quantitative) language inclusion problem is undecidable for mean-payoff automata and the decidability is open for discounted-sum automata, whereas the (quantitative) simulation reduce to mean-payoff games and discounted-sum games, which admit pseudo-polynomial time algorithms.\r\n\r\nIn this work, we study (quantitative) simulation for weighted automata with Büchi acceptance conditions, i.e., we generalize fair simulation from non-weighted automata to weighted automata. We show that imposing Büchi acceptance conditions on weighted automata changes many fundamental properties of the simulation games. For example, whereas for mean-payoff and discounted-sum games, the players do not need memory to play optimally; we show in contrast that for simulation games with Büchi acceptance conditions, (i) for mean-payoff objectives, optimal strategies for both players require infinite memory in general, and (ii) for discounted-sum objectives, optimal strategies need not exist for both players. While the simulation games with Büchi acceptance conditions are more complicated (e.g., due to infinite-memory requirements for mean-payoff objectives) as compared to their counterpart without Büchi acceptance conditions, we still present pseudo-polynomial time algorithms to solve simulation games with Büchi acceptance conditions for both weighted mean-payoff and weighted discounted-sum automata.","lang":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"record":[{"status":"public","id":"1066","relation":"later_version"}]}}