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Orientation and decision-making for Soccer Based on Sports Analytics and AI:A Systematic Review

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 37-57页

作者： Zhiqiang Pu Yi Pan Shijie Wang Boyin Liu Min Chen Hao Ma Yixiong Cui the School of Artificial Intelligence University of Chinese Academy of Sciences the Institute of Automation Chinese Academy of Sciences the Nanjing Artificial Intelligence Research of IA the School of Sports Engineering Beijing Sport University

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

关键词： Artificial intelligence(AI) decision-making football review soccer sports analytics

Evolutionary decision-making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

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Engineering 2024年第2期33卷 108-120页

作者： Kang Yuan Yanjun Huang Shuo Yang Zewei Zhou Yulei Wang Dongpu Cao Hong Chen College of Electronic and Information Engineering Tongji UniversityShanghai 201804China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201210China School of Automotive Studies Tongji UniversityShanghai 201804China School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China

decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

关键词： Autonomous driving decision-making Motion planning Deep reinforcement learning Model predictive control

Toward Trustworthy decision-making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

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Engineering 2024年第2期33卷 77-89页

作者： Xiangkun He Wenhui Huang Chen Lv School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

关键词： Autonomous vehicle decision-making Reinforcement learning Adversarial attack Safety guarantee

A NovelMethod for Determining Tourism Carrying Capacity in a decision-making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1951-1979页

作者： Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityP.O.Box 13-5053ByblosLebanon Department of Software and CMPSI Konju National UniversityCheonan31080Korea

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

关键词： q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator

Research on Public Engineering Emergency decision-making Based on Multi-Granularity Language Information

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Journal of Architectural Research and Development 2024年第1期8卷 32-37页

作者： Huajun Liu Zengqiang Wang General Office of Sichuan Provincial Government Chengdu 610000Sichuan ProvinceChina School of Management Xihua UniversityChengdu 610000Sichuan ProvinceChina

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

关键词： Public engineering Emergency Multi-granularity language decision-making

A Blind Spot in the Reframing of a Universe of Possibles: Towards a Suitable Model for decision-making Theory and A.I.

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Journal of Applied Mathematics and Physics 2024年第6期12卷 2172-2189页

作者： Gilbert Giacomoni AgroParisTech (Paris Saclay University) & Paris Saclay Applied Economics (UMR 210) INRAE 22 place de lAgronomie Palaiseau France

Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about possible states of nature, in order to make a better judgment while taking new evidence into account. Such a scientific model proposed for the general theory of decision-making, like all others in general, whether in statistics, economics, operations research, A.I., data science or applied mathematics, regardless of whether they are time-dependent, have in common a theoretical basis that is axiomatized by relying on related concepts of a universe of possibles, especially the so-called universe (or the world), the state of nature (or the state of the world), when formulated explicitly. The issue of where to stand as an observer or a decision-maker to reframe such a universe of possibles together with a partition structure of knowledge (i.e. semantic formalisms), including a copy of itself as it was initially while generalizing it, is not addressed. Memory being the substratum, whether human or artificial, wherein everything stands, to date, even the theoretical possibility of such an operation of self-inclusion is prohibited by pure mathematics. We make this blind spot come to light through a counter-example (namely Archimedes’ Eureka experiment) and explore novel theoretical foundations, fitting better with a quantum form than with fuzzy modeling, to deal with more than a reference universe of possibles. This could open up a new path of investigation for the general theory of decision-making, as well as for Artificial Intelligence, often considered as the science of the imitation of human abilities, while being also the science of knowledge representation and the science of concept formation and reasoning.

关键词： decision-making Innovation Universe of Possibles A.I. Quantum Form Fuzzy Modeling

decision-making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

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Journal of Behavioral and Brain Science 2024年第2期14卷 47-63页

作者： Paul E. Plonski Jasmin Vassileva Ryan Shahidi Paul B. Perrin William Carter Lance L. Goetz Amber Brochetti James M. Bjork Institute for Drug and Alcohol Studies Virginia Commonwealth University Richmond VA USA Richmond Veterans Affairs Medical Center Richmond VA USA Department of Psychiatry Virginia Commonwealth University Richmond VA USA Department of Psychology Virginia Commonwealth University Richmond VA USA Department of Physical Medicine and Rehabilitation Virginia Commonwealth University Richmond VA USA Department of Spinal Cord Injury Cleveland Veterans Affairs Medical Center Cleveland OH USA Department of Pharmacology and Toxicology Virginia Commonwealth University Richmond VA USA

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

关键词： Spinal Cord Injury Self-Care decision-making Paraplegia Impulsive Behavior Health Care

Supporting decision-making processes for selecting fire safety measures for road tunnels

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Journal of Traffic and Transportation Engineering(English Edition) 2022年第3期9卷 473-489页

作者： Panagiotis Ntzeremes Konstantinos Kirytopoulos School of Mechanical Engineering National Technical University of AthensAthens 10682Greece

Selecting fires safety measures for road tunnels relies mainly on strict regulatory requirements. However, the choice should also be based on many different criteria and ranking of alternatives should take place. Existing methods exhibit lack in dealing rigorously with measures’ selection amongst different alternatives. This paper contributes to the body of knowledge by proposing a novel method, named EVADE, which aims to incorporate diverse stakeholders’ views and provide a meaningful ranking of alternatives.To do so, it estimates the tunnel level of safety taking into account only any standard measures existing. Subsequently, the performance of additional measures is examined.Then, a list of the most significant criteria that are valuable to judge the appropriateness of selected measures is introduced. The relative importance amongst the decision criteria is calculated through the Analytic Hierarchy Process, based on the expert opinion. Sensitivity analysis through Monte Carlo simulation is embedded to allow for a meaningful prioritization of the decision criteria. Thus, the alternatives’ ranking comes as a distribution instead of a single number, providing the decision-maker richer information for selecting the most suitable measure(s) according to the specific tunnel situation. At last, a typical tunnel is examined to showcase the utilization of the method.

关键词： Road tunnel Critical infrastructure decision-making Quantitative-risk-assessment

A multi-enterprise quality function deployment paradigm with unstructured decision-making in linguistic contexts

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Journal of Systems Engineering and Electronics 2023年第4期34卷 966-980页

作者： PENG Jian’gang XIA Guang SUN Baoqun WANG Shaojie Intelligent Manufacturing Institute Hefei University of TechnologyHefei 230009China Institute of Automotive Engineering Hefei University of TechnologyHefei 230009China

This paper presents an operational framework of unstructured decision-making approach involving quality function deployment(QFD)in an uncertain linguistic context.Firstly,QFD is extended to the multi-enterprise paradigm in a real-world manufacturing environment.Secondly,hesitant fuzzy linguistic term sets(HFLTSs),which facilitate the management and handling of information equivocality,are designed to construct a house of quality(HoQ)in the product planning process.The technique of computing with words is applied to bridge the gap between mechanisms of the human brain and machine processes with fuzzy linguistic term sets.Thirdly,a multi-enterprise QFD pattern is formulated as an unstructured decision-making problem for alternative infrastructure project selection in a manufacturing organization.The inter-relationships of cooperative partners are directly matched with a back propagation neural network(BPNN)to construct the multi-enterprise manufacturing network.The resilience of the manufacturing organization is considered by formulating an outranking method on the basis of HFLTSs to decide on infrastructure project alternatives.Finally,a real-world example,namely,the prototype manufacturing of an automatic transmission for a vehicle,is provided to illustrate the effectiveness of the proposed decision-making approach.

关键词： decision-making quality function deployment(QFD) uncertainty fuzzy set