Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

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.

以色彩为媒介的Design Thinking在艺术设计中的创新实践

色彩作为Design Thinking中的重要媒介,在艺术设计创新实践中具有重要潜力。本文探讨了以色彩为媒介的Design Thinking在艺术设计创新实践中的应用。色彩不仅在Design Thinking中占据重要地位,而且对人类的情绪和行为产生影响,同时在设计中起着至关重要的作用。将Design Thinking引入艺术实践领域,旨在培养艺术创新思维和问题解决能力,强调创新实践和跨学科合作。职业院校在艺术设计创新实践中面临着诸多挑战,但也存在机遇。通过案例分析,本文展示了如何实施基于色彩的Design Thinking艺术设计实践以及这种创新对艺术创新能力的积极影响。

基于“Design Thinking”理念的职业院校创新创业人才培养模式

随着我国经济不断发展和社会进步,创新创业已经成为推动经济增长的关键驱动力,在2015年全国人民代表大会上,李克强总理将“大众创业、万众创新”明确列为我国经济增长的“双引擎”之一,彰显了创新创业对于推动社会发展的重要地位。为响应国家政策,各高校积极开展创新创业教育,将其纳入高等教育综合改革的重要议程,成为培养未来人才的重要途径。本文分析了“Design Thinking”理念下职业院校创新创业人才培养的必要性,围绕新时期专业院校创新创业人才培养的要求,提出了基于“Design Thinking”理念的职业院校创新创业人才培养模式的有效策略,为职业院校人才培养效率的强化提供了参考性意见。

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

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.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

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.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

The Effects of Disruptive Thinking on the Food Industry*

This study aimed to understand disruptive thinking and how its ideas can change the food industry. This was achieved by identifying, studying, and understanding the impacts, current trends, and different disruptive ideas and innovations emerging in the food industry. The study was conducted through interpretive research philosophy by carrying out secondary data collection processes, where both qualitative and quantitative information was presented. Deductive approaches were also selected to apply existing theories and models, which were used to construct research hypotheses and present detailed findings. The study finds that, with disruptive thinking, enhancements in the product life cycle, new flavors, and improvements in food packaging have been possible. The supply chain, which is always considered a complex part of the food industry, has been streamlined, offering greater transparency and real-time tracking and improving quality control across distribution systems.

Exploring Critical Thinking Dispositions among Fourth Year BSc Nursing Students of Various Colleges of Nursing, Pakistan

Background: Critical Thinking (CT) dispositions in nursing are prominent predictors of competence in delivering high-quality care, and of professionalism, in newly graduated nurses. CT skills, in isolation of CT dispositions, do not guarantee success in the workplace, because Critical Thinking Dispositions (CTD) are important elements of intellectual reasoning that simulate a person towards using the CT skills. Therefore, nursing educational programs should promote lifelong learning rather than focusing on transferring the content of nursing knowledge only. And for this purpose, quality education is the key. Education should focus on teaching from diverse perspectives, incorporating various teaching learning strategies that are congruent with the modern era. Purpose: The purpose of this study is to explore critical thinking dispositions among final year Baccalaureate Nursing students of various military colleges of nursing, in Pakistan. Methodology: A descriptive qualitative exploratory study design was used to investigate the CTD of BSc final year nursing students. The study population included twelve willing nursing students, from six military colleges across the country. Demographic information and consent was taken from the participants of the study. In-depth interviews, through a semi structured interview guide, and probes were used to obtain data related to personal experiences of CTD amongst the nursing students. Results: Data analysis showed two broad themes: 1) Perceptions of CT, and 2) Experiences of CT dispositions. In theme one, the emerging category was: Clarity of CT;whereas in theme two, the categories that emerged were: a) Truth Seeking, b) Open Mindedness, c) Inquisitiveness, and d) Self Organization. Conclusion: The findings of the study revealed positive dispositions towards truth seeking, open mindedness, and self-organization, whereas disposition towards inquisitiveness was weak. Self-confidence and maturity also emerged as positive factors that the students possessed. This study recommends that faculty and learners should extend their concept of CTD, and emphasizes its application in daily routine. Additionally, faculty should modify their instructional strategies and focus on the cultivation of dispositions of inquisitiveness, curiosity, and allow questioning by students in the class.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

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

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.

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

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.

Applying Design Thinking in Ecotourism Curriculum Design: The Educational Design Ladder

The design thinking approach transforms student’s learning process to connect with real-world experience, making connections between knowledge and improving critical thinking and problem-solving capability. Previous research has focused on what design thinking is taught (content) and how it is taught (learning modes and assessment). However, there needs to be an integrative approach in the context of ecotourism education. Therefore, applying Wrigley and Straker’s Educational Design Ladder mode, this study develops an Ecotourism Design Thinking Curriculum framework to combine the ecotourism content, teaching/learning mode and assessment.

Reexamining Positivity of Thinking in Waiting for Godot Through Camus’s Absurdism

Absurdity originated from French existentialism,and Martin Esslin,in categorizing Waiting for Godot as a theater of the absurd,ignores the positive aspects of Camus’s absurdism and regards that as an existential tragedy of mankind.However,the author Samuel Beckett himself did not approve of such a definition,believing that Esslin’s definition of theater of the absurd was too judgmental and pessimistic.Through the imagery of boots,ropes,hats and baggage in Waiting for Godot,Beckett reveals the rootlessness,constraint and agony of the minds of people born into absurdity in the early 20th century.The luck of Lucky is Beckett’s use of irony:people who have lost the ability to think for themselves escape the absurdity and dystopia of life and descend to slaves.Gogo and Didi,who are waiting and expecting,and thinking in the midst of absurdity,represent“Godot”,the symbol of hope.Their endurance in waiting,akin to Camus’s Sisyphus,underscores a form of existential defiance that challenges the notion of absolute pessimism in absurdist literature.

English-Chinese Translation of Science and Technology Texts Based on the Differences Between Chinese and Western Modes of Thinking

As a tool for human communication,the use of language is affected by the way of thinking in cross-cultural communication.Due to the differences between Chinese and Western cultures,the way of thinking varies under the influence of such cultural differences.In the process of translation,the influence of the different thinking mode on translation activities cannot be ignored.At present,a lot of studies and researches have focused on the influence of Chinese and Western thinking mode on translation.However,it is still worthwhile to study what specific influence the difference between Chinese and Western thinking mode will exert on the translation process of science texts and what kind of measure should be taken to solve the negative influence brought by the differences when we are translating.This paper aims to discuss the influence of thinking differences between Chinese and Western countries on the process of English-Chinese translation of science and technology from the perspectives of long sentences,passive voice and nominalization,and how the translators should appropriately adjust their thinking pattern and use reasonable skills to eliminate the potential negative interference caused by the difference and achieve a better transformation between English and Chinese.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

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.

On the Teaching Design of Graduates’EAP Course:Enhancing Language Proficiency and Critical Thinking Skills

This paper explores the integration of the bridge-in,objectives,pre-assessment,participatory activities,post-assessment and summary(BOPPPS)teaching model within the context of the post-graduates Academic English course.It discusses how this structured approach can effectively enhance students’language proficiency,foster critical thinking skills,and align with the multifaceted objectives of advanced English language education.The study provides a detailed examination of each BOPPPS component as applied to the post-graduates Academic English curriculum,supported by theoretical underpinnings and practical implications.

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

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.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.