We introduce a new approach to solving path-finding problems under uncertainty by representing them as probabilistic models and applying domain-independent inference algorithms to the models. This approach separates problem representation from the inference algorithm and provides a framework for efficient learning of path-finding policies. We evaluate the new approach on the Canadian Traveler Problem, which we formulate as a probabilistic model, and show how probabilistic inference allows high performance stochastic policies to be obtained for this problem.
Quchan University of Technology（库昌理工大学）；University of Melbourne（墨尔本大学）
This contribution reviews critically the existing entropy measures for probabilistic hesitant fuzzy sets (PHFSs), and demonstrates that these entropy measures fail to effectively distinguish a variety of different PHFSs in some cases. In the sequel, we develop a new axiomatic framework of entropy measures for probabilistic hesitant fuzzy elements (PHFEs) by considering two facets of uncertainty associated with PHFEs which are known as fuzziness and nonspecificity. Respect to each kind of uncertainty, a number of formulae are derived to permit flexible selection of PHFE entropy measures. Moreov…
A Labelling Framework for Probabilistic Argumentation
CSIRO（联邦科学与工业研究组织）；University of Brescia（布雷西亚大学）；Chinese Academy of Sciences（中国科学院）；University of Bologna（博洛尼亚大学）；European University Institute（欧洲大学学院）
Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples
University of Bremen（不来梅大学）；City University London（伦敦城市大学）；University of Edinburgh（爱丁堡大学）；University of Luxembourg（卢森堡大学）；University of Amsterdam（阿姆斯特丹大学）
Process mining is a subfield of process science that analyzes event data collected in databases called event logs. Recently, novel types of event data have become of interest due to the wide industrial application of process mining analyses. In this paper, we examine uncertain event data. Such data contain meta-attributes describing the amount of imprecision tied with attributes recorded in an event log. We provide examples of uncertain event data, present the state of the art in regard of uncertainty in process mining, and illustrate open challenges related to this research direction.
University of Duisburg-Essen（杜伊斯堡-埃森大学）；University of Leicester（莱斯特大学）
This paper exploits extended Bayesian networks for uncertainty reasoning on Petri nets, where firing of transitions is probabilistic. In particular, Bayesian networks are used as symbolic representations of probability distributions, modelling the observer's knowledge about the tokens in the net. The observer can study the net by monitoring successful and failed steps. An update mechanism for Bayesian nets is enabled by relaxing some of their restrictions, leading to modular Bayesian nets that can conveniently be represented and modified. As for every symbolic representation, the question is h…
Norwegian University of Science and Technology（挪威科技大学）；United States Naval Academy（美国海军学院）
Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. Despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification in a rea…
Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
Weill Cornell Medicine, Cornell University, New York, NY（韦尔·科恩医学中心，康奈尔大学，纽约，纽约）；Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY（电气与计算机工程系，史泰文斯布鲁克大学，史泰文斯布鲁克，纽约）
The application of machine learning (ML) techniques in wireless communication domain has seen a tremendous growth over the years especially in the wireless sensing domain. However, the questions surrounding the ML model's inference reliability, and uncertainty associated with its predictions are never answered or communicated properly. This itself raises a lot of questions on the transparency of these ML systems. Developing ML systems with probabilistic modeling can solve this problem easily, where one can quantify uncertainty whether it is arising from the data (irreducible error or aleotoric…
Probabilistic electrical power demand forecasting with uncertainty quantification
Nepal College of Information Technology（尼泊尔信息技术学院）；Nepal Telecommunications Authority（尼泊尔电信管理局）；Nepal Electricity Authority（尼泊尔电力局）；University of Bergen（卑尔根大学）；Western Norway University of Applied Sciences（挪威西部应用科学大学）
Frankfurt School of Finance & Management（法兰克福金融与管理学院）；University of Bologna（博洛尼亚大学）
There are two reasons why uncertainty may not be adequately described by Probability Theory. The first one is due to unique or nearly-unique events, that either never realized or occurred too seldom for frequencies to be reliably measured. The second one arises when one fears that something may happen, that one is not even able to figure out, e.g., if one asks: "Climate change, financial crises, pandemic, war, what next?" In both cases, simple one-to-one cognitive maps between available alternatives and possible consequences eventually melt down. However, such destructions reflect into the cha…
Technion - Israel Institute of Technology（以色列理工学院）
It is a long-standing objective to ease the computation burden incurred by the decision making process. Identification of this mechanism's sensitivity to simplification has tremendous ramifications. Yet, algorithms for decision making under uncertainty usually lean on approximations or heuristics without quantifying their effect. Therefore, challenging scenarios could severely impair the performance of such methods. In this paper, we extend the decision making mechanism to the whole by removing standard approximations and considering all previously suppressed stochastic sources of variability.…
Michigan State University（密歇根州立大学）；Argonne National Laboratory（阿贡国家实验室）；Brookhaven National Laboratory（布鲁克海文国家实验室）
Robust machine learning models with accurately calibrated uncertainties are crucial for safety-critical applications. Probabilistic machine learning and especially the Bayesian formalism provide a systematic framework to incorporate robustness through the distributional estimates and reason about uncertainty. Recent works have shown that approximate inference approaches that take the weight space uncertainty of neural networks to generate ensemble prediction are the state-of-the-art. However, architecture choices have mostly been ad hoc, which essentially ignores the epistemic uncertainty from…
Two-Step MV-DeepONet: Probabilistic Operator Learning for Uncertainty Propagation Driven by Random Input Fields
School of Mathematics and Physics, North China Electric Power University（华北电力大学数理学院）；School of Energy Power and Mechanical Engineering, North China Electric Power University（华北电力大学能源动力与机械工程学院）
