SHF: Small: Reasoning Rigorously About Probabilistic Programs

SHF：小：对概率程序进行严格推理

• 批准号: 1320069
• 负责人: Sriram Sankaranarayanan
• 金额: $ 39.56万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320069&HistoricalAwards=false
• 关键词: SHF; Small; Reasoning; Rigorously; About

As society becomes increasingly reliant on software systems to helpplan medical treatments, predict the future climate, guide investmentsin financial markets, and mine noisy data to infer scientific facts,the risk that uncertainties in the input data and defects in thesoftware can create false alarms, or a false sense of security, ishigh. This project will help address the gap between highly complexprobabilistic programs and tools for reasoning about them.Probabilistic programs arise in important everyday applications thatinclude medical, engineering and financial risk analysis/decisionmaking systems, large-scale simulations, data mining, sensor noisefiltering algorithms for cyber-physical systems, and randomizedalgorithms. The presence of input uncertainties and randomness builtinto the behavior of the programs can cause undesirable behaviors andvariable performance. Therefore, it is important to accuratelypredict the probabilities of such undesirable behaviors, and expectedvalues for important performance measures.This project investigates automatic program analysis tools forprobabilistic programs that will model the sources of uncertaintyappropriately, and infer bounds on the probabilities of assertions andexpectations of performance measures. Two flavors of inferenceprocedures are being investigated: symbolic procedurescombining decision procedures with the theory of martingales andstatistical procedures using statistical hypothesis testingmethods to infer probabilistic annotations with statisticalguarantees.

随着社会越来越依赖于软件系统来HELPPLAN医疗治疗，预测未来的气候，指导金融市场中的投资以及噪音数据来推断科学事实，因此输入数据中的不确定性和Thesoftware中的不确定性会产生错误警报的风险，或者会产生错误的警报，或者是错误的安全感。该项目将有助于解决高度复杂的培训计划与有关其推理的工具之间的差距。在重要的日常应用中出现了培训程序，包括医学，工程和财务风险分析/决策系统，大规模仿真，数据挖掘，传感器噪音过滤算法，用于网络物理系统系统以及随机化的Allagiendical Alcorithmits以及随机的级别级别。输入不确定性和随机性的存在构建程序的行为可能会导致不良行为和可变性的性能。 因此，重要的是要准确预测这种不良行为的概率，以及对重要绩效指标的预期价值。本项目调查了自动程序分析工具，即良好的程序，这些工具将模拟不合适的范围，并推断出绩效指标的概率和绩效指标的概率。正在研究两种推理程序的口味：使用统计假设测试方法，符合符号的决策程序和统计程序的决策程序，以统计保证来推断概率注释。