Tools and algorithms for the construction and analysis of systems : 26th International Conference, TACAS 2020, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25-30, ...最新文献_第6页

We consider Markov decision processes (MDPs) in which the transition probabilities and rewards belong to an uncertainty set parametrized by a collection of random variables. The probability distributions for these random parameters are unknown. The problem is to compute the probability to satisfy a temporal logic specification within any MDP that corresponds to a sample from these unknown distributions. In general, this problem is undecidable, and we resort to techniques from so-called scenario optimization. Based on a finite number of samples of the uncertain parameters, each of which induces an MDP, the proposed method estimates the probability of satisfying the specification by solving a finite-dimensional convex optimization problem. The number of samples required to obtain a high confidence on this estimate is independent from the number of states and the number of random parameters. Experiments on a large set of benchmarks show that a few thousand samples suffice to obtain high-quality confidence bounds with a high probability.

本文研究了马尔可夫决策过程，其中转移概率和奖励属于由随机变量集合参数化的不确定性集合。这些随机参数的概率分布是未知的。问题是计算满足与这些未知分布的样本相对应的任何MDP内的时间逻辑规范的概率。一般来说，这个问题是不确定的，我们求助于所谓的场景优化技术。该方法基于有限数量的不确定参数样本，每个样本都会产生一个MDP，通过求解一个有限维的凸优化问题来估计满足规格的概率。在这个估计上获得高置信度所需的样本数量与状态的数量和随机参数的数量无关。在大量基准测试上的实验表明，几千个样本足以获得高概率的高质量置信界限。

{"title":"Scenario-Based Verification of Uncertain MDPs.","authors":"Murat Cubuktepe, Nils Jansen, Sebastian Junges, Joost-Pieter Katoen, Ufuk Topcu","doi":"10.1007/978-3-030-45190-5_16","DOIUrl":"https://doi.org/10.1007/978-3-030-45190-5_16","url":null,"abstract":"<p><p>We consider Markov decision processes (MDPs) in which the transition probabilities and rewards belong to an uncertainty set parametrized by a collection of random variables. The probability distributions for these random parameters are unknown. The problem is to compute the probability to satisfy a temporal logic specification within any MDP that corresponds to a sample from these unknown distributions. In general, this problem is undecidable, and we resort to techniques from so-called scenario optimization. Based on a finite number of samples of the uncertain parameters, each of which induces an MDP, the proposed method estimates the probability of satisfying the specification by solving a finite-dimensional convex optimization problem. The number of samples required to obtain a high confidence on this estimate is independent from the number of states and the number of random parameters. Experiments on a large set of benchmarks show that a few thousand samples suffice to obtain high-quality confidence bounds with a high probability.</p>","PeriodicalId":93056,"journal":{"name":"Tools and algorithms for the construction and analysis of systems : 26th International Conference, TACAS 2020, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25-30, ...","volume":"12078 ","pages":"287-305"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7402411/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38231758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Mora - Automatic Generation of Moment-Based Invariants 基于矩的不变量的自动生成

Tools and algorithms for the construction and analysis of systems : 26th International Conference, TACAS 2020, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25-30, ...

Pub Date : 2020-03-13 DOI: 10.1007/978-3-030-45190-5_28

E. Bartocci, L. Kovács, Miroslav Stankovič

引用次数: 18