Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efficiently and accurately is difficult. Modeled as dynamic Bayesian networks, these processes have discrete variables with very large domains and extremely high dimensionality. In this paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an extension of dynamic Bayesian networks (DBNs) to first-order logic. RDBNs are a generalization of dynamic probabilistic relational models (DPRMs), which we had proposed in our previous work to model dynamic uncertain domains. We first extend the Rao-Blackwellised particle filtering described in our earlier work to RDBNs. Next, we lift the assumptions associated with Rao-Blackwellization in RDBNs and propose two new forms of particle filtering. The first one uses abstraction hierarchies over the predicates to smooth the particle filters estimates. The second employs kernel density estimation with a kernel function specifically designed for relational domains. Experiments show these two methods greatly outperform standard particle filtering on the task of assembly plan execution monitoring.
Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution. The state of the art for decision-making in airline disruption management involves a heuristic human-centric approach that does not categorically study uncertainty in proactive and reactive processes for managing airline schedule disruptions. Hence, this paper introduces an uncertainty transfer function model (UTFM) framework that characterizes uncertainty for proactive airline disruption management before schedule execution, …
Probabilistic Relational Model Benchmark Generation
LARODEC Laboratory（拉罗德克实验室）；ISG（突尼斯高等管理学院）；Université de Tunis（突尼斯大学）；DUKe research group（DUKe研究组）；LINA Laboratory UMR 6241（LINA实验室（法国国家科研中心联合研究单位6241））；University of Nantes（南特大学）；DataForPeople
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；singularIT GmbH（singularIT有限公司）；University of Münster（明斯特大学）；University of Lübeck（吕贝克大学）
Tensor tree learns hidden relational structures in data to construct generative models
Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；The University of Tokyo（东京大学）；Institute for Physics of Intelligence, The University of Tokyo（东京大学智能物理研究所）；Institute for Solid State Physics, The University of Tokyo（东京大学固态物理研究所）；Trans-scale Quantum Science Institute, The University of Tokyo（东京大学跨尺度量子科学研究所）
Data cleaning is naturally framed as probabilistic inference in a generative model of ground-truth data and likely errors, but the diversity of real-world error patterns and the hardness of inference make Bayesian approaches difficult to automate. We present PClean, a probabilistic programming language (PPL) for leveraging dataset-specific knowledge to automate Bayesian cleaning. Compared to general-purpose PPLs, PClean tackles a restricted problem domain, enabling three modeling and inference innovations: (1) a non-parametric model of relational database instances, which users' programs custo…
Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration
Henan University, China（河南大学）；Department of Statistics, LMU Munich（慕尼黑大学统计系）；MCML Munich（慕尼黑MCML）；CISPA Helmholtz Center for Information Security, Saarbrücken, Germany（萨尔布吕肯德国海德堡中心信息安全研究所）
This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data. Given a set of relations defined over a collection of domains, the model first infers multiple non-overlapping clusters of relations using a top-level Chinese restaurant process. Within each cluster of relations, a Dirichlet process mixture is then used to partition the domain entities and model the probability distribution of relation values. The HIRM generalizes the standard infinite relational model and can be used for a variety o…
LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification
University of California, Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；University of California, San Diego（加州大学圣地亚哥分校）；University of California, Los Angeles（加州大学洛杉矶分校）
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Duke University（杜克大学）；TU Darmstadt（达姆施塔特工业大学）
We consider the problem of Approximate Dynamic Programming in relational domains. Inspired by the success of fitted Q-learning methods in propositional settings, we develop the first relational fitted Q-learning algorithms by representing the value function and Bellman residuals. When we fit the Q-functions, we show how the two steps of Bellman operator; application and projection steps can be performed using a gradient-boosting technique. Our proposed framework performs reasonably well on standard domains without using domain models and using fewer training trajectories.
Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets
Department of Technologies and Information Systems, Universidad de Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学技术与信息系统系）；Warwick Manufacturing Group, University of Warwick（华威大学华威制造集团）
Stanford University（斯坦福大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；University of California, Berkeley（加州大学伯克利分校）
While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is limited. In this paper, we propose a group-aware relational reasoning approach (named EvolveHypergraph) with explicit inference of the underlying dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction. In addition to the edges between a pair of nodes (i.e., agents), we propose to infer hyperedges that adaptively connect multiple nodes to enable group-awa…
The University of Sydney（悉尼大学）；The University of Melbourne（墨尔本大学）；JD Explore Academy（京东探索研究院）
The generalization of model-based reinforcement learning (MBRL) methods to environments with unseen transition dynamics is an important yet challenging problem. Existing methods try to extract environment-specified information $Z$ from past transition segments to make the dynamics prediction model generalizable to different dynamics. However, because environments are not labelled, the extracted information inevitably contains redundant information unrelated to the dynamics in transition segments and thus fails to maintain a crucial property of $Z$: $Z$ should be similar in the same environment…
