Many real-world planning domains involve diverse information sources, external entities, and variable-reliability agents, all of which may impact the confidence, risk, and sensitivity of plans. Humans reviewing a plan may lack context about these factors; however, this information is available during the domain generation, which means it can also be interwoven into the planner and its resulting plans. This paper presents a provenance-based approach to explaining automated plans. Our approach (1) extends the SHOP3 HTN planner to generate dependency information, (2) transforms the dependency information into an established PROV-O representation, and (3) uses graph propagation and TMS-inspired algorithms to support dynamic and counter-factual assessment of information flow, confidence, and support. We qualified our approach's explanatory scope with respect to explanation targets from the automated planning literature and the information analysis literature, and we demonstrate its ability to assess a plan's pertinence, sensitivity, risk, assumption support, diversity, and relative confidence.
Authoring and Management of Transparent Research Integrity Assessments of Randomised Clinical Trial Publications Using LLM-assisted Tools and Provenance Knowledge Graphs
Interdisciplinary Institute, University of Aberdeen（阿伯丁大学跨学科研究所）；University of Aberdeen（阿伯丁大学）；Indiana University（印第安纳大学）；University of Manchester（曼彻斯特大学）；Aberdeen Centre for Evaluation, University of Aberdeen（阿伯丁大学阿伯丁评估中心）；University of Auckland（奥克兰大学）
Analytic software tools and workflows are increasing in capability, complexity, number, and scale, and the integrity of our workflows is as important as ever. Specifically, we must be able to inspect the process of analytic workflows to assess (1) confidence of the conclusions, (2) risks and biases of the operations involved, (3) sensitivity of the conclusions to sources and agents, (4) impact and pertinence of various sources and agents, and (5) diversity of the sources that support the conclusions. We present an approach that tracks agents' provenance with PROV-O in conjunction with agents' …
University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Loyola University Chicago（芝加哥洛约拉大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard University（哈佛大学）；George Mason University（乔治梅森大学）
Daily progress notes are common types in the electronic health record (EHR) where healthcare providers document the patient's daily progress and treatment plans. The EHR is designed to document all the care provided to patients, but it also enables note bloat with extraneous information that distracts from the diagnoses and treatment plans. Applications of natural language processing (NLP) in the EHR is a growing field with the majority of methods in information extraction. Few tasks use NLP methods for downstream diagnostic decision support. We introduced the 2022 National NLP Clinical Challe…
Urban Comfort Assessment in the Era of Digital Planning: A Multidimensional, Data-driven, and AI-assisted Framework
Department of Architecture, National University of Singapore（新加坡国立大学建筑系）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）；Department of Real Estate, National University of Singapore（新加坡国立大学房地产系）
URSA: Chemistry-Aware Benchmark for Utilitarian Retrosynthesis Assessment
Insilico Medicine AI Limited（Insilico Medicine AI有限公司）；Independent researcher（独立研究者）；Insilico Medicine Canada Inc.（Insilico Medicine加拿大公司）；Insilico Medicine Hong Kong Ltd.（Insilico Medicine香港有限公司）
The transition to a new low emission energy future results in a changing mix of generation and load types due to significant growth in renewable energy penetration and reduction in system inertia due to the exit of ageing fossil fuel power plants. This increases technical challenges for electrical grid planning and operation. This study introduces a new decomposition approach to account for the system security for short term planning using conventional machine learning tools. The immediate value of this work is that it provides extendable and computationally efficient guidelines for using supe…
Raytheon Technologies Research Center（雷神技术研究中心）；University of Colorado Boulder（科罗拉多大学博尔德分校）
We summarize our efforts to date in developing a framework for generating succinct human-understandable competency self-assessments in terms of machine self confidence, i.e. a robot's self-trust in its functional abilities to accomplish assigned tasks. Whereas early work explored machine self-confidence in ad hoc ways for niche applications, our Factorized Machine Self-Confidence framework introduces and combines several aspects of probabilistic meta reasoning for algorithmic planning and decision-making under uncertainty to arrive at a novel set of generalizable self-confidence factors, which…
