我们研究了在不确定的环境中运行的机器人面临的计划问题,对状态不完整,嘈杂和/或不精确的行动。本文确定了一个新的问题子类,该阶级模拟了设置信息,在该设置中,只有通过某些外源过程,该过程会间歇性地揭示信息,该过程定期提供状态信息。几个实用领域符合该模型,包括激发我们研究的特定情况:远程成像增强行星探索的自主导航。为了注视着有效的专业解决方案方法,我们检查了该子类实例的结构。它们导致马尔可夫的决策过程具有指数较大的动作空间,但由于这些动作包括更多原子元素的序列,因此可以通过比较不同信息假设下的策略来建立绩效界限。这提供了一种系统地构建性能界限的方法。这样的界限很有用,因为与它们赋予的见解结合在一起,它们可以采用基于边界的方法来有效地获得高质量的解决方案。我们提出的经验结果证明了它们对所考虑的问题的有效性。上述内容还提到了时间时间为这些问题所扮演的独特作用 - 更具体地说:直到信息揭示的时间 - 我们在这方面发现并讨论了几个有趣的微妙之处。
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可以将一组个人或组织之间的战略互动建模为在网络上玩的游戏,在网络上,玩家的回报不仅取决于他们的行动,还取决于邻居的行动。从观察到的游戏结果(平衡动作)中推断网络结构是一个重要的问题,对于经济学和社会科学中的许多潜在应用。现有方法主要需要与游戏相关的效用函数的知识,在现实世界中,这通常是不现实的。我们采用类似变压器的体系结构,该体系结构正确说明了问题的对称性,并在没有明确了解效用功能的情况下学习了从平衡动作到游戏网络结构的映射。我们使用合成和现实世界数据在三种不同类型的网络游戏上测试我们的方法,并证明其在网络结构推理中的有效性和优于现有方法的卓越性能。
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在异构机器人网络上进行计算负载共享是一个有希望的方法,可以将机器人能力和效率作为极端环境中的团队提高。然而,在这种环境中,通信链路可以是间歇性的,并且与云或因特网的连接可能是不存在的。在本文中,我们介绍了用于多机器人系统的通信感知,计算任务调度问题,并提出了整数线性程序(ILP),该程序(ILP)优化了异构机器人网络中的计算任务分配,占网络机器人的计算能力对于可用(和可能的时变)通信链接。我们考虑调度由依赖关系图建模的一组相互依赖的必需任务和可选任务。我们为共享世界,分布式系统提供了一项备份的调度架构。我们验证了ILP制定和不同计算平台中的分布式实现,并在模拟场景中,偏向于月球或行星探索方案。我们的研究结果表明,与没有计算负载共享的类似系统相比,所提出的实施方式可以优化提高时间表以允许三倍增加所执行的奖励任务的数量(例如,科学测量)。
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船上自治技术,如规划和调度,识别科学目标和基于内容的数据摘要,将导致令人兴奋的新空间科学任务。然而,尚未研究具有此类船上自治能力的经营任务的挑战,这是足以在使命概念中考虑的细节水平。这些自主功能需要更改当前的操作流程,实践和工具。我们制定了一个案例研究,以评估使运营商和科学家通过促进地面人员和车载算法之间的共同模型来运营自主航天器所需的变化。我们评估使运营商和科学家能够向航天器传达所需的新的操作工具和工作流程,并能够重建和解释船上和航天器状态的决定。这些工具的模型用于用户学习,了解过程和工具在实现共享理解框架方面的有效性,以及在运营商和科学家有效实现特派团科学目标的能力。
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According to the latest trend of artificial intelligence, AI-systems needs to clarify regarding general,specific decisions,services provided by it. Only consumer is satisfied, with explanation , for example, why any classification result is the outcome of any given time. This actually motivates us using explainable or human understandable AI for a behavioral mining scenario, where users engagement on digital platform is determined from context, such as emotion, activity, weather, etc. However, the output of AI-system is not always systematically correct, and often systematically correct, but apparently not-perfect and thereby creating confusions, such as, why the decision is given? What is the reason underneath? In this context, we first formulate the behavioral mining problem in deep convolutional neural network architecture. Eventually, we apply a recursive neural network due to the presence of time-series data from users physiological and environmental sensor-readings. Once the model is developed, explanations are presented with the advent of XAI models in front of users. This critical step involves extensive trial with users preference on explanations over conventional AI, judgement of credibility of explanation.
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Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classification algorithm based on long short term memory (LSTM) and TOPSIS is proposed to identify and rank, respectively, adoption features. Our theoretical model is validated over a reference model by articulating key players, services, architecture, functionalities. Qualitative survey is conducted among users by considering technology, innovation and resistance issues, to formulate hypotheses on key adoption factors.
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Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood. This leads to sparse prototypes that improve clustering interpretability. We introduce an expectation-maximisation (EM) algorithm for this estimation and explore the trade-off between the sparsity term and the likelihood one with a path following algorithm. The model's behaviour is studied on simulated data and, we show the advantages of the approach on real data benchmark. We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.
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It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.
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In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.
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Graph Neural Networks (GNNs) have become increasingly important in recent years due to their state-of-the-art performance on many important downstream applications. Existing GNNs have mostly focused on learning a single node representation, despite that a node often exhibits polysemous behavior in different contexts. In this work, we develop a persona-based graph neural network framework called PersonaSAGE that learns multiple persona-based embeddings for each node in the graph. Such disentangled representations are more interpretable and useful than a single embedding. Furthermore, PersonaSAGE learns the appropriate set of persona embeddings for each node in the graph, and every node can have a different number of assigned persona embeddings. The framework is flexible enough and the general design helps in the wide applicability of the learned embeddings to suit the domain. We utilize publicly available benchmark datasets to evaluate our approach and against a variety of baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a variety of important tasks including link prediction where we achieve an average gain of 15% while remaining competitive for node classification. Finally, we also demonstrate the utility of PersonaSAGE with a case study for personalized recommendation of different entity types in a data management platform.
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