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Smart home platform supporting decentralized adaptive automation control 20

Smart home platform supporting decentralized adaptive automa...

引用

35th Annual ACM symposium on Applied Computing (SAC)

作者： Arcaini, Paolo Mirandola, Raffaela Riccobene, Elvinia Scandurra, Patrizia Arrigoni, Alberto Bosc, Daniele Modica, Federico Pedercini, Rita Natl Inst Informat Tokyo Japan Politecn Milan Milan Italy Univ Milan Dipartimento Informat Milan Italy Univ Bergamo DIGIP Bergamo Italy

ISBN: (纸本)9781450368667

Smart Home is an exemplar application domain of the Internet of Things (IoT). In particular, smart home automation processes that manage the interactions with the home devices and integrate all their (possibly interfering) services, introduce new challenges and call for modern engineering practices, software platforms, and computational intelligence. A key example is the need to achieve a more efficient integration between design and runtime aspects in the automation process, taking into account that unanticipated situations can occur in the home environment and require a smart home system to deal with them at runtime. In this paper, using an application example, we foresee how to architect future smart home systems in order to endow them with self-adaptation capabilities allowing decentralized control and device interconnection. Namely, we propose to exploit feedback control loop architectures (e.g., composite MAPE feedback loops) to design and manage the home automation processes, as alternative software architectural solutions to those of smart home platforms currently available.

关键词： Smart home platforms software architecture styles for smart home systems architecture-based self-adaptation MAPE-K OpenHAB

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A control Loop-based Algorithm for Operational Transformation 14

A Control Loop-based Algorithm for Operational Transformatio...

引用

ieee 14th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Gadea, Cristian Ionescu, Bogdan Ionescu, Dan Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781728173764

Operational Transformation (OT) has emerged as a viable theoretical principle for the implementation of real-time collaboration applications. In such systems, the collaboration consists of operations generated by members of a group who are performing concurrent actions on the same document or content. This powerful multi-user co-editing has been researched ever since the seminal works of the late 1980s. As the web evolved into a dominant platform for content consumption and creation, classes of algorithms like OT and Conflict-free Replicated Data Types (CRDT) have enabled flexible content synchronization for applications such as online word processors. Despite their long history in academia, OT and CRDT continue to have unsolved issues due to the centralized approach required for scalable and reliable web-based document editing. This paper proposes a control Loop-based OT approach based on a serverless architecture and on Finite State Automata (FSA). A control loop principle is used to design a series of algorithms for distributed conflict resolution. The proposed architecture consists of a series of blocks which internally contain a number of multi-level Finite State Machines. The architecture of the new serverless approach for OT is introduced and the basic FSAs that model the co-editing processes are described. Cases encountered in the dynamics of the co-editing processes were modeled to prove that the essential OT properties of causality preservation, convergence, and intention preservation are all satisfied. Simulation results are given at the end of the paper.

关键词： control Loop Architecture Decision Making Automata Collaborative Software Platforms Distributed Computing Systems Consistency and Concurrency Finite State Automata Real-Time Automata Real-Time Systems

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A Simplified Variant for Graph Convolutional Network Based Knowledge Graph Completion Model

A Simplified Variant for Graph Convolutional Network Based K...

引用

ieee International Conference on Data Science in Cyberspace (DSC)

作者： Yashen Wang Qiang Li Yi Zhang Xuecheng Zhang Zhuoya Ju Jian Sun National Engineering Laboratory for Risk Perception and Prevention (RPP) Key Laboratory of Cognition and Intelligence Technology (CIT) China Academy of Electronics and Information Technology Artificial Intelligence Institute of CETC Beijing China The State Key Laboratory for Management and Control of Complex Systems Scientific and Technological Innovation Center of ARI Institute of Automation Chinese Academy of Sciences Beijing China CETC Academy of Electronics and Information Technology Group Co. Ltd. Beijing China Khoury College of Computer Science Northeastern University U.S.A Scientific and Technological Innovation Center of ARI Beijing China Qi An Xin Technology Group Inc. Beijing China

Knowledge graphs (KGs) serve as useful resources for various applications including machine learning, data mining, and artificial intelligence. Knowledge Graph Completion (KGC) aims at reasoning over known (observed) facts and infer the missing (unobserved) relationships, to improve KG's coverage. Many recent works have laid stress on leveraging Graph Convolutional Networks (GCNs) for KGC task, which obtain good representation learning ability for graph-structured data, and have achieved satisfactory results. However, GCN-based researches continue to improve these lines of models by introducing an increasingly complex feature space. Especially, the hyper-parameter space of conventional GCN-based KGC model is too huge, resulting in low computational efficiency and excessive storage pressure. To overcome this problem, this paper investigates a simplified and generic variant for GCN-based KGC model, and shows that simple changes in the architecture of the underlying architecture (with a Subtraction Operator and an Addition Operator), can compte with state-of-the-art GCN-based models. Therefore, this paper proposes a easy-to-accomplish plug-in unit, which could be loaded into any current GCN-based KGC architectures, and help them maximize the maintenance of native model's original performance while significantly reducing parameter scale.

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Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning

Measuring Data Quality for Dataset Selection in Offline Rein...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Phillip Swazinna Steffen Udluft Thomas Runkler Siemens Technology & Technical University of Munich Munich Germany Siemens Technology Munich Germany

ISBN: (纸本)9781728190495

Recently developed offline reinforcement learning algorithms have made it possible to learn policies directly from pre-collected datasets, giving rise to a new dilemma for practitioners: Since the performance the algorithms are able to deliver depends greatly on the dataset that is presented to them, practitioners need to pick the right dataset among the available ones. This problem has so far not been discussed in the corresponding literature. We discuss ideas how to select promising datasets and propose three very simple indicators: Estimated relative return improvement (ERI) and estimated action stochasticity (EAS), as well as a combination of the two (COI), and empirically show that despite their simplicity they can be very effectively used for dataset selection.

关键词： Correlation Data integrity Industrial control Current measurement Decision making Reinforcement learning Benchmark testing

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Combining Utility AI and MCTS Towards Creating Intelligent Agents in Video Games, with the Use Case of Tactical Troops: Anthracite Shift

Combining Utility AI and MCTS Towards Creating Intelligent A...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Maciej Świechowski Daniel Lewiński Rafal Tyl QED Software Warsaw Poland

ISBN: (纸本)9781728190495

Tactical Troops: Anthracite Shift is a squad-based tactics game with complex environment. It is a commercial game released on the Steam platform. This paper is authored by its original developers. It presents how the AI -driven players, that are featured in the game, have been designed and implemented. Their procedure of operation is based on a hierarchical combination of Monte Carlo Tree Search and Utility AI. Although these methods alone have already been applied to video games, there have not been published works that employ such a combination. Solutions to creating the AI in commercial games are rarely open to the public, which motivated us even more. The quality of the AI agents has been assessed from two perspectives: playing strength and believability.

关键词： Industries Monte Carlo methods Decision making Games control systems Complexity theory Intelligent agents

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Population based Reinforcement Learning

Population based Reinforcement Learning

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Kyle W. Pretorius Nelishia Pillay University of Pretoria Pretoria South Africa

ISBN: (纸本)9781728190495

Genetic algorithms have recently seen an increase in application due to their highly scalable nature. Enabling more efficient utilization of processing power that has become readily available. This study introduces Population based reinforcement learning (PBRL), a method that hybridizes a GA with a policy gradient reinforcement learning algorithm. This combination not only enables more scalable policy optimization, but also helps mitigate some of the common weaknesses of policy gradient algorithms. Furthermore, PBRL is also extended to include automatic hyper-parameter tuning, which is used to evaluate the impact that such tuning can have on the performance of the policy gradient algorithm being used. Experiments comparing these methods are conducted on a number of continuous control problems simulated by MuJoCo. Results show that PBRL is capable of outperforming a commonly used policy gradient algorithm, while also producing results in nearly one fifth the time. It is also observed that the addition of automatic hyperparameter tuning can be greatly beneficial for environments where well tuned hyper-parameters are not known.

关键词： Training Sociology Reinforcement learning Artificial neural networks Learning (artificial intelligence) Statistics Tuning

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End-to-end Visual Navigation with Intrinsic Motivation in 3D Maze-like Environments

End-to-end Visual Navigation with Intrinsic Motivation in 3D...

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2021 China automation Congress, CAC 2021

作者： Li, Peng Ruan, Xiao-Gang Zhu, Xiao-Qing Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781665426473

Learning to navigate in visually rich environment with sparse reward is a long-standing problem in developing AI agent. In this paper, inspired by spontaneous exploration behaviour in animals, we proposed an end-to-end visual navigation method which use intrinsic reward to motivate explore-exploit strategy. Firstly, in order to directly obtain control policy from pixel-level input and implement end-to-end training, we take deep reinforcement learning as basic navigation framework. Secondly, we allow the agent to create reward for itself, and the intrinsic reward is related to episode memory and include two parts: (1) the frequency of agent reach a state is recorded, and these counts are then used to compute bonus according to the classic count-based method;(2) we also take temporal distance as a basis for distributing bonus, and the magnitude of it is determined by environment steps between current observation and those in memory. Finally, such intrinsic reward is summed up with the real task reward, thus making agent is able to use the combined reward to learn control policy. We test our approach in 3D maze-like environment in DMlab, and validate its navigation performance through different reward scenarios. The experiment results show our approach can get effective goal-driven behaviour from raw sensory input and outperform the baselines across all tasks. Furthermore, the agent equipped with our intrinsic motivation appear to explore faster in no reward environment. © 2021 ieee

关键词： Reinforcement learning

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On Modifications Towards Improvement of the Exploitation Phase for SOMA Algorithm with Clustering-aided Migration and Adaptive Perturbation Vector control

On Modifications Towards Improvement of the Exploitation Pha...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Tomas Kadavy Adam Viktorin Michal Pluhacek Stanislav Kovar Faculty of Applied Informatics Tomas Bata University in Zlin Zlin Czech Republic

ISBN: (纸本)9781728190495

This paper represents the next step in the development of the recently proposed single objective metaheuristic algorithm - Self-Organizing Migrating Algorithm with CLustering-aided migration and adaptive Perturbation vector control (SOMA-CLP). The CEC 2021 single objective bound-constrained optimization benchmark testbed was used for the performance evaluation of the modifications of the algorithm. The presented modifications were invoked by the results of CEC 2021 competition, where the SOMA-CLP ranked 7th out of 9 competing algorithms. This paper introduces three modifications of population organization process focusing on one particular phase of the SOMA-CLP algorithm aimed at exploitation. All results were compared and tested for statistical significance against the original variant using the Friedman rank test. The algorithm modification and analysis of the results presented here can be inspiring for other researchers working on the development and modifications of evolutionary computing techniques.

关键词： Performance evaluation Perturbation methods Heuristic algorithms Metaheuristics Sociology Clustering algorithms Focusing

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Stoichiometry control of the two gas reactive sputtering process 19

Stoichiometry control of the two gas reactive sputtering pro...

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ieee Joint 19th International symposium on computational intelligence and Informatics / 7th International Conference on Recent Achievements in Mechatronics, automation, Computer Sciences and Robotics

作者： Madardsz, Robert Rossi Kelemen, Andras Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary Sapientia Hungarian Univ Transylvania Dept Elect Engn Targu Mures Romania

ISBN: (纸本)9781728156255

The objective of the paper is the synthesis of a control structure for a two-gas reactive magnetron sputtering process, using a model that is suitable for modeling the coverage of the substrate with different compounds of the particles participating in the process. Assuming its observability, the ternary compound coverage is controlled by means of the gas flow of oxygen supplied in a continuous fashion, with the oxygen partial pressure limitation dependent on the nitrogen partial pressure. On the other hand, the set value of the nitrogen partial pressure depends on the set value of the discharge current, which is controlled by means of the nitrogen gas flow. The control characteristics have been derived and the dynamic behavior of the proposed stoichiometry control structure has been simulated based on the parameters of a laboratory sputtering chamber, which is intended to be used for the experimental validation of the simulation results.

关键词： magnetron sputtering two-gas reactive sputtering partial pressure control stoichiometry titanium oxynitride TiOxNy

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Simulink/MATLAB based Comparison of Neural and Basic Tracking control for an Autonomous Surface Vessel for Situation Awareness Applications 19

Simulink/MATLAB based Comparison of Neural and Basic Trackin...

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ieee Joint 19th International symposium on computational intelligence and Informatics / 7th International Conference on Recent Achievements in Mechatronics, automation, Computer Sciences and Robotics

作者： Astrov, Igor Udal, Andres Pedai, Andrus Sell, Raivo Tallinn Univ Technol Dept Mech & Ind Engn Tallinn Estonia Tallinn Univ Technol Dept Software Sci Tallinn Estonia

ISBN: (纸本)9781728156255

The intelligent control of autonomous vehicles has become one of the high priority tasks in development of Cyber-Physical Systems (CPS) for Industry 4.0 and Situation Awareness (SA) applications. Moving of an Autonomous Surface Vessel (ASV) to the desired area of sea is a challenging task to achieve high level of autonomy under adverse conditions. Paper focuses on the development of methodology of model-based modeling and simulation of cyber-physical nonlinear system of ASV in Simulink/MATLAB software environment. The comparison between predictive neural control and basic tracking feedback control has been presented for two maneuvers: the turn at different angles and the circular motion at final destination. Simulation results confirm the necessity and superiority of neural controller approach.

关键词： autonomous surface vessels feedback control predictive neurocontrollers situational awareness

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