Artificial Intelligence and Robotics Commons^™

Multi-Agent Simulation For Online Fresh Food Autonomous Delivery, Miaojia Lu, Chengyuan Huang, Jing Teng

Journal of System Simulation

Abstract: Autonomous delivery can solve the last-mile delivery problems of low efficiency, high manual cost, and potential safety hazard. The autonomous delivery of the online fresh food in urban communities is discussed and a data-driven agent-based platform with the actual spatial-temporal demand is built. Three kinds of agents including the autonomous vehicles, customers, and distribution center and the simulation environment based on the actual road network are construct. To achieve the objectives of the minimum total operating costs and maximum customer satisfaction, the different static and dynamic order dispatch strategies and the route planning strategies with the principle of …

Denoising Algorithm Based On Multi-Feature Non-Local Mean Filtering For Monte Carlo Rendered Images, Kai Yang, Chunyi Chen, Xiaojuan Hu, Haiyang Yu

Journal of System Simulation

Abstract: Aiming at the rendering noise in Monte Carlo synthesized images induced by the low light-path sampling rate, a denoising algorithm based on the multi-feature non-local-mean filtering is proposed. The gradient image of the scene's albedo information is calculatedwith the canny operator, and a guided filter together with the said gradient image is employed to prefilter the normal vector image. The structural similarity of the sub-blocks in the prefiltered normal vector image is calculated and the improved weights of the non-local mean filter are computed according to the logarithmic value of the reciprocal of the structural similarity. The improved …

Multi-Uavs 3d Path Planning Method Based On Random Strategy Search, Sen Zhang, Mengyan Zhang, Jingping Shao, Jiexin Pu

Journal of System Simulation

Abstract: In view of the difficulty of the traditional path planning method without energy consumption constraints to meet the emergency rescue requirements in the complex mountain operation environment, a three-dimensional path planning algorithm for multi-UAVs is proposed based on LSTM-DPPO(long short-term memory-distributed proximal policy optimization) framework. The LSTM long and short-term memory neural network is used to extract the important characteristic state information sequence of the multiple unmanned aerial vehicles in their respective flight process. After repeated iteration and updating, an optimal network parameter model is obtained. Combined with the energy consumption, the optimal 3D detection path is generated. …

Simulation Of Multi-Layer Ship Evacuation System Based On Improved A* Algorithm, Dun Meng, Zhuo Hu, Huajun Zhang

Journal of System Simulation

Abstract: Aiming at the low efficiency of emergency evacuation at sea, an emergency evacuation system based on improved A* algorithm is proposed. Based on the network flow model, the traversal mode of the adjacency node is used to complete the path search, and the influence of the path personnel density and path obstacles is added to the calculation of the cost, which makes the algorithm more practical. In order to improve the efficiency of the algorithm, the node optimization of the network is carried out, and a multi-path optimal scheme is proposed in the case of single layer with multiple …

Image Center Layout Optimization Method Based On Improved Genetic Algorithm, Zhijie Li, Haoqi Shi, Changhua Li, Jie Zhang

Journal of System Simulation

Abstract: Aiming at the layout optimization methods of image center being influenced by the subjective factors and low level of automation, a method of combining systematic layout planning(SLP) with the improved genetic algorithm is proposed. The layout scheme generated by SLP improves the initial population of the genetic algorithm and increases the diversity of the initial population. In order to improve the efficiency of optimization, the improved algorithm updates the crossover probability and mutation probability adaptively according to the evolution stages and the fitness value of the individuals. On the basis of the layout area model and multi-objective optimization mathematical …

Airport Flight Transit Support Time Collaborative Planning Modeling Based On Stn, Bin Chen, Yue Liu, Yalei Yang

Journal of System Simulation

Abstract: Under the constraint of resources, the collaborative planning of airport flight transit support time is one of the effective methods to improve airport operation efficiency. Based on Simple Temporal Network (STN), a planning model of flight transit support time is established. Based on the temporal decoupling, the shortest path matrix simplification, and the distance graph solving of STN task model considering resources, the method of collaborative planning of flight transit support time for airport considering resources is obtained. The comparison results of the simulation and the actual data show that STN task model considering resources can optimize the airport …

Vehicle Detection Method Based On Multi Scale Feature Fusion, Yin Wang, Feixiang Wang, Qianlai Sun

Journal of System Simulation

Abstract: Vehicle detection is the important research content and hotspot in the intelligent transportation. Aiming at the low detection accuracy and poor small-scale recognition effect of the traditional vehicle detection algorithm, an improved detection method based on YOLOv4(you only look once v4) is proposed to improve the detection performance of small target vehicles in traffic scenes. By redesigning the YOLOv4 network, the MobileNetv2 deep separable convolution module is used to replace the traditional convolution, and the convolutional block attention module (CBAM) attention module is integrated into the feature extraction network to ensure the detection accuracy of the model and reduce …

A Hybrid Algorithm Based On Seeker Optimization Algorithm And Salp Swarm Algorithm For Pid Parameters Optimization, Shaomi Duan, Huilong Luo, Haipeng Liu

Journal of System Simulation

Abstract: Aiming at the premature convergence of seeker optimization algorithm(SOA) during optimizing the global problems, a new SOA-SSA hybrid algorithm based on seeker optimization algorithm and salp swarm algorithm (SSA) is proposed.The SOA-SSA algorithm is based on a double population evolution strategy, in which some individuals of the population are evolved by seeker optimization algorithm and the rest are evolved from salp swarm algorithm. The individuals in SOA and SSA both employ an information sharing mechanism to realize the coevolution. These strategies increase the diversity of the population and avoid the premature convergence. The experimental results show that …

Application Of Improved Q Learning Algorithm In Job Shop Scheduling Problem, Yejian Zhao, Yanhong Wang, Jun Zhang, Hongxia Yu, Zhongda Tian

Journal of System Simulation

Abstract: Aiming at the job shop scheduling in a dynamic environment, a dynamic scheduling algorithm based on an improved Q learning algorithm and dispatching rules is proposed. The state space of the dynamic scheduling algorithm is described with the concept of "the urgency of remaining tasks" and a reward function with the purpose of "the higher the slack, the higher the penalty" is disigned. In view of the problem that the greedy strategy will select the sub-optimal actions in the later stage of learning, the traditional Q learning algorithm is improved by introducing an action selection strategy based on the …

Visual Inspection Model Of Uav Cluster Based On Improved Pigeon Flock Hierarchy, Qi Chen, Haoyang Cui

Journal of System Simulation

Abstract: Aim at UAV being vulnerable to the environmental interference and the low efficiency of the traditional single-person-UAV model in the transmission line inspection, a visual inspection model for the power line by UAV is proposed based on the improved pigeon flock hierarchy. The initial landmark point of the UAV is generated based on GPS coordinates of the aircraft-carrying vehicle and the tower to be inspected, and the movement trajectory is planned. The return point of the UAV is used to update the initial landmark of onward UAV, which realizes the dynamic handover between the work-exchanging UAV, and the landmark …

Design Of Interactive Simulated Water Gun Fire Fighting Training System Based On Steam Vr, Cheng Lu, Xuesheng Jin

Journal of System Simulation

Abstract: In order to save the fire fighting training resources and increase the immersion and experience of VR training, an interactive simulated water gun fire fighting training system based on Steam VR is designed. By using the Hall sensors and signal conversion circuit boards to collect and transmit the signal of the simulated water gun, and by using the Unity3D engine combined with the VIVE head-mounted display to build and present VR fire scene. The gun is controlled through C# programming to complete the interaction with the virtual fire scene. The system is evaluated by a post-questionnaire survey …

Application Of Observability In Performance Evaluation Of Photosynthesis Model, Hongnai Gao, Lijiang Fu, Qian Xia, Ya Guo

Journal of System Simulation

Abstract: The photochemical reaction of photosynthesis involves a variety of physiological substances that cannot be directly measured. By modeling the control system, the state of these physiological substances can be es-timated based on the chlorophyll fluorescence, but the reliability of the state estimation is not given in all the reference documents. In response to this problem, based on the photochemical reaction kinetic model, the observability of the nonlinear system is introduced to evaluate the reliability of the state estimation. Aiming at the existing observability methods lacking the direct comparability due to the different dimensions of the components of different states, …

Fuzzy Super-Twisting Second Order Sliding Mode Trajectory Tracking Control For Robotic Manipulator, Baolin Du, Dachang Zhu, Yihua Pan

Journal of System Simulation

Abstract: A fuzzy super-twisting second order sliding mode control method is proposed for the uncertainties of the model error and external disturbance on the trajectory tracking accuracy of robotic manipulator. Based on the dynamic model of the robotic, a new non-singular terminal sliding mode manifold is designed, and an improved super-twisting algorithm is used to design the second order sliding mode controller. In order to solve the problem that the matching disturbance can only be compensated under the condition of the known disturbance boundary in the sliding mode control, the fuzzy logic algorithm is used to carryout the online compensation …

Research On Fire Emergency Evacuation Simulation Based On Cooperative Deep Reinforcement Learning, Lingjia Ni, Xiaoxia Huang, Hongga Li, Zibo Zhang

Journal of System Simulation

Abstract: The fire accident is a major threat to the public safety, in which the high temperature, toxic and harmful gases seriously interfer the selection of the evacuation routes. Deep reinforcement learning is introduced into the research of emergency evacuation simulation, and a cooperative double deep Q network algorithm is proposed for the multi-agent environment. A fire scene model that changes dynamically over time is established to provide the real-time information on the distribution of the dangerous areas for the evacuation. The independent agent neural networks are integrated and the multi-agent unified deep neural network is established to realize the …

Machine Learning With Kay, Lasith Niroshan, James Carswell

Conference Papers

Computational power is very important when training Deep Learning (DL) models with large amounts of data (Wooldridge, 2021). Hence, High-Performance Computing (HPC) can be leveraged to reduce computational cost, and the Irish Centre for High-End Computing (ICHEC) provides significant infrastructure and services for research and development to both academia and industry. A portion of ICHEC's HPC system has been allocated for institutional access, and this paper presents a case study of how to use Kay (Ireland's national supercomputer) in the remote sensing domain. Specifically, this study uses clusters of Kay Graphics Processing Units (GPUs) for training DL models to extract …

Learning To Generalize Dispatching Rules On The Job Shop Scheduling, Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal, Martin Takac

Machine Learning Faculty Publications

This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem. A well-known technique to improve generalization is to learn on increasingly complex instances using Curriculum Learning (CL). However, as many works in the literature indicate, this technique might suffer from catastrophic forgetting when transferring the learned skills between different problem sizes. To address this issue, we introduce a novel Adversarial Curriculum Learning (ACL) strategy, …

An Empirical Study On Sampling Approaches For 3d Image Classification Using Deep Learning, Nicholas Michelette

Theses and Dissertations

A 3D classification method requires more training data than a 2D image classification method to achieve good performance. These training data usually come in the form of multiple 2D images (e.g., slices in a CT scan) or point clouds (e.g., 3D CAD modeling) for volumetric object representation. The amount of data required to complete this higher dimension problem comes with the cost of requiring more processing time and space. This problem can be mitigated with data size reduction (i.e., sampling). In this thesis, we empirically study and compare the classification performance and deep learning training time of PointNet utilizing uniform …

What-Is And How-To For Fairness In Machine Learning: A Survey, Reflection, And Perspective, Zeyu Tang, Jiji Zhang, Kun Zhang

Machine Learning Faculty Publications

Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections among them are not clearly addressed. In this paper, we review and reflect on various fairness notions previously proposed in machine learning literature, and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. We also consider fairness inquiries from a dynamic perspective, and further consider the long-term impact that is induced by current prediction and decision. In light of the differences in the characterized fairness, we present a flowchart …

Learning To Control Under Time-Varying Environment, Yuzhen Han, Ruben Solozabal, Jing Dong, Xingyu Zhou, Martin Takac, Bin Gu

Machine Learning Faculty Publications

This paper investigates the problem of regret minimization in linear time-varying (LTV) dynamical systems. Due to the simultaneous presence of uncertainty and non-stationarity, designing online control algorithms for unknown LTV systems remains a challenging task. At a cost of NP-hard offline planning, prior works have introduced online convex optimization algorithms, although they suffer from nonparametric rate of regret. In this paper, we propose the first computationally tractable online algorithm with regret guarantees that avoids offline planning over the state linear feedback policies. Our algorithm is based on the optimism in the face of uncertainty (OFU) principle in which we optimistically …

Flecs: A Federated Learning Second-Order Framework Via Compression And Sketching, Artem Agafonov, Dmitry Kamzolov, Rachael Tappenden, Alexander Gasnikov, Martin Takac

Machine Learning Faculty Publications

Inspired by the recent work FedNL (Safaryan et al, FedNL: Making Newton-Type Methods Applicable to Federated Learning), we propose a new communication efficient second-order framework for Federated learning, namely FLECS. The proposed method reduces the high-memory requirements of FedNL by the usage of an L-SR1 type update for the Hessian approximation which is stored on the central server. A low dimensional 'sketch' of the Hessian is all that is needed by each device to generate an update, so that memory costs as well as number of Hessian-vector products for the agent are low. Biased and unbiased compressions are utilized to …

Offline Reinforcement Learning With Causal Structured World Models, Zheng-Mao Zhu, Xiong-Hui Chen, Hong-Long Tian, Kun Zhang, Yang Yu

Machine Learning Faculty Publications

Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environment. Previous model-based offline RL methods learn fully connected nets as world-models to map the states and actions to the next-step states. However, it is sensible that a world-model should adhere to the underlying causal effect such that it will support learning an effective policy generalizing well in unseen states. In this paper, We first provide theoretical results that causal world-models can outperform plain world-models for offline RL by incorporating the causal structure into the generalization error …

What’S So Artificial And Intelligent About Artificial Intelligence? A Conceptual Framework For Ai, Rebekah L. H. Rice

SPU Works

There is currently a good deal of attention being focused on artificial intelligence, broadly speaking, and deep learning, specifically. The attention is warranted, as these technologies are predicted to affect our collective lives in innumerable ways even beyond their already expansive social reach. There is much to consider regarding the benefits and potential harms of AI. And of course there are the apocalyptic musings about super-intelligent machines running amok, bringing science fiction scenarios uncomfortably close to anticipated reality. But productively engaging in discussions about the ethical and social implications of AI, and about which sorts of futures it is reasonable …

A Theological Framework For Reflection On Artificial Intelligence, Michael D. Langford

SPU Works

The theological questions before us in a digital age are pressing. What does God think of AI? Is AI good or evil? Will AI save us? What sort of future will AI give us? In what follows, I want to briefly introduce a few theological concepts that will hopefully help equip us for theological reflection on AI. We will begin with the question of epistemology, or how it is that we come by knowledge; in the realm of theology, this centers on revelation. We will then touch on the doctrine of creation, including the understanding of what it means to …

Artificial Intelligence And Theological Personhood, Michael D. Langford

SPU Works

Can AI be a person? What does God tell us about humanity and personhood? These are questions of theological anthropology and involve inquiring after the nature of humanity as God’s creation and what God wills for human personhood.

To address these inquiries, we will look at three biblical texts that bear on issues of theological anthropology, hopefully garnering some theological resources to consider the anthropological status of AI. Specifically, we will look at three “creation” texts that necessarily deal with the nature of human personhood within the divine economy of salvation history. The first is Genesis 1 and 2, which …

Reinforcement In The Information Revolution, Phillip M. Baker

SPU Works

This chapter will outline what it means to be a behaving human and how AI makes sense of these concepts. It will then explore possible near-future implications of our remarkable progress in understanding how human behavior works with the assistance of AI from a neurobiological basis. A focus on understanding the reinforcement mechanisms of the brain will reveal the consequences of ceding control of so much of our brain-environment interactions to AI. It will conclude by offering a potential Christian response to this digital reality from a uniquely Anabaptist perspective.

An Introduction To Artificial Intelligence, Carlos R. Arias

SPU Works

This chapter explores the evolution of artificial intelligence, starting with the first ideas of Alan Turing, going through the promises of its inception, and landing in our current state, when AI invokes a sense of power and awe. Next, the chapter will provide a summary of different technologies related to AI and machine learning, such as deep neural networks, to help the reader distinguish different terminologies. The chapter will end with a discussion of some potential tendencies concerning how AI may be used or evolve in the near future, and some questions about the technology in the long term.

Sin And Grace, Bruce D. Baker

SPU Works

The theological lens of sin and grace gives a broader and deeper viewpoint than mere ethics. Ethical analysis is of course useful and necessary, but ethics alone is not enough. Ethics apart from a robust, holistic understanding of humans as persons-in-communion will remain mired in reductionist thinking about human dignity and morality. Therefore, this final chapter addresses the ethical issues of AI through the lens of sin and grace.

Epilogue: A Litany For Faithful Engagement With Artificial Intelligence, Bruce D. Baker

SPU Works

A litany is a thoughtfully organized prayer for use in public worship by the church, or as a personal devotional practice by individuals. This seems a fitting way to close our reflection on AI, faith, and the future. Prayer will be essential to our faithful response to the new opportunities and challenges AI brings. Our hope is that this litany will serve as a practical guide to thoughtful invocation of the Holy Spirit in prayers for wisdom and discernment, and in the daily disciplines of spiritual growth.

21st Century Learning Skills And Artificial Intelligence, David Wicks, Michael Paulus

SPU Works

The chapter explores four concepts important for learning and AI in the twenty-first century—creativity, critical thinking, communication, and collaboration (the “4Cs”)—as well as reflections on the theological significance of creativity and community.

Automation And Apocalypse: Imagining The Future Of Work, Michael Paulus

SPU Works

This chapter provides an orientation to the history of technology, work, and the theology of work and then explores three visions of the future of work—a literary dystopia, a philosophical utopia, and a theological apocalypse—as resources for understanding the significance of work and imagining its future. In the first vision, found in Kurt Vonnegut’s speculative novel Player Piano, automation leads to the end of meaningful work and nearly renders humans obsolete. This dystopic vision reveals the value of human work but remains skeptical about our ability to preserve it against the advances of automation. The second vision comes from …