Behavior Decision Model Research Articles

Resource scheduling optimization for industrial operating system using deep reinforcement learning and WOA algorithm

Industrial operating systems (IOS) are essential for supporting smart manufacturing, particularly in managing and utilizing heterogeneous production resources through resource instantiation scheduling (RIS) technique. However, RIS faces the challenge of efficiently selecting optimal resource service compositions from numerous options with varying quality of service. To boost the solving of the RIS problem and improve the quality of the solution, this paper proposes a novel hybrid algorithm, named DWOA, based on the whale optimization algorithm (WOA) and deep reinforcement learning (DRL). It first incorporates the DRL algorithm to learn experience from the historical data regarding exploration and exploitation in the WOA search process and train an optimal behavior decision model. Subsequently, utilizing the trained model, the DWOA can effectively guide the search agent in achieving a better balance between global exploration and local exploitation, thereby enhancing its convergence speed and solution quality. The effectiveness and efficiency of the DWOA approach are evaluated by the CEC2017 benchmark functions and RIS problems with various scales, compared with 11 state-of-the-art methods. The experimental results indicate that our method converges faster and produces better solutions for RIS problems.

Construction of Power System Network Security Defense Behavior Decision-making Model based on Artificial Intelligence Technology

According to the needs of power grid monitoring architecture and information security cooperation protection, this project builds a multi-level, deeply distributed active security cooperation defense mode. A complete implementation method is proposed from the perspective of model architecture and function mechanism. The optimal defense strategy based on grey correlation is studied according to the characteristics of cooperation between regions. In this way, the coordination between the equipment is realized to achieve multi-level protection from the host layer to the security equipment layer and then to the network layer. Multiple detection mechanisms are used to realize the comprehensive detection and integrated judgment of abnormal documents in the cloud environment. This provides maximum protection for cloud users. Experiments show that this method can effectively suppress the malicious attacks of malicious users and reduce the damage caused by viruses. In this way, both the cloud and the customer are protected.

Optimal scheduling and trading in joint electricity and carbon markets

The collaborative development of the electricity and carbon markets can reduce transaction costs, stimulate energy conservation and emission reductions, and accelerate the social transition to low carbon. In this paper, we present a comprehensive review of the current state and policy challenges of electricity-carbon trading and present an in-depth analysis of key research directions and technical details of electricity-carbon collaboration in the context of large-scale access to renewable energy. Then, by constructing a multi-agent behavioral decision-making model, we refine the decision-making mechanism of electricity-carbon synergy, simulated market clearing outcomes under different mechanisms, and proposed policies and incentive mechanisms to promote electricity-carbon trading. Furthermore, leveraging the massive data in energy systems, we design a carbon measurement method and carbon emission flow tracking method for low-carbon energy companies, achieving real-time carbon accounting for the power system. We establish a collaborative simulation model of the electricity and carbon markets, enabling an accurate prediction of the day-ahead carbon emission intensity. This study provides a new research framework and complete technical route for electricity-carbon coordination, which can further promote the low-carbon transformation of the energy system.

Optimizing Multi-Vessel Collision Avoidance Decision Making for Autonomous Surface Vessels: A COLREGs-Compliant Deep Reinforcement Learning Approach

Automatic collision avoidance decision making for vessels is a critical challenge in the development of autonomous ships and has become a central point of research in the maritime safety domain. Effective and systematic collision avoidance strategies significantly reduce the risk of vessel collisions, ensuring safe navigation. This study develops a multi-vessel automatic collision avoidance decision-making method based on deep reinforcement learning (DRL) and establishes a vessel behavior decision model. When designing the reward function for continuous action spaces, the criteria of the “Convention on the International Regulations for Preventing Collisions at Sea” (COLREGs) were adhered to, taking into account the vessel’s collision risk under various encounter situations, real-world navigation practices, and navigational complexities. Furthermore, to enable the algorithm to precisely differentiate between collision avoidance and the navigation resumption phase in varied vessel encounter situations, this paper incorporated “collision avoidance decision making” and “course recovery decision making” as state parameters in the state set design, from which the respective objective functions were defined. To further enhance the algorithm’s performance, techniques such as behavior cloning, residual networks, and CPU-GPU dual-core parallel processing modules were integrated. Through simulation experiments in the enhanced Imazu training environment, the practicality of the method, taking into account the effects of wind and ocean currents, was corroborated. The results demonstrate that the proposed algorithm can perform effective collision avoidance decision making in a range of vessel encounter situations, indicating its efficiency and robust generalization capabilities.

An Interaction Behavior Decision-Making Model of Service Robots for the Disabled Based on Human–Robot Empathy

An Interaction Behavior Decision-Making Model of Service Robots for the Disabled Based on Human–Robot Empathy

Applications and Challenges of Computer Vision in Autonomous Driving

Abstract The rapid evolution of autonomous driving technology has spurred deeper exploration into its multifaceted domains. This paper focuses on enhancing the intelligence and safety of autonomous driving systems through the application of computer vision. Specifically, the integration of EIoU loss and Varifocal Loss functions within the YOLOv5 algorithm facilitates the training of higher-quality samples at reduced costs. This enhanced YOLOv5 algorithm is adept at detecting traffic entities and incorporates the Matrix NMS algorithm to further refine the loss function adjustments. Moreover, this study employs the proximal policy optimization algorithm to construct a vehicular behavioral decision model for autonomous driving. This model utilizes cubic polynomial equations to depict lane-changing trajectories and introduces a safety distance rule to mitigate collision risks during autonomous maneuvers. Comparative analyses reveal that the modified YOLOv5 algorithm surpasses its predecessors—YOLOv3 and YOLOv4—in terms of detection accuracy and processing speed. Notably, the improved YOLOv5 algorithm exhibits lower network loss values than its original version, achieving faster and more stable convergence. In practical applications, the algorithm successfully identified and labeled 20,025 intelligent vehicular targets, with initial testing accuracy reaching 96.12%. This accuracy improved to 96.36% following the application of EIoU adaptive tuning to reduce risk-free class loss, and further fine-tuning elevated the accuracy to 96.54%. The integration of computer vision into autonomous driving technology thus holds significant promise for advancing the field.

A User’s guide for inference in models defined by moment inequalities

Models defined by moment inequalities have become a standard modeling framework for empirical economists, spreading over a wide range of fields within economics. From the point of view of an empirical researcher, the literature on inference in moment inequality models is large and complex, including multiple survey papers that document the non-standard features these models possess, the main novel concepts behind inference in these models, and the most recent developments that bring advances in accuracy and computational tractability. In this paper we present a guide to empirical practice intended to help applied researchers navigate all the decisions required to frame a model as a moment inequality model and then to construct confidence intervals for the parameters of interest. We divide our template into four main steps: (a) a behavioral decision model, (b) moving from the decision model to a moment inequality model, (c) choosing a test statistic and critical value, and (d) accounting for computational challenges. We split each of these steps into a discussion of the “how” and the “why”, and then illustrate how to take these steps to practice in an empirical application that studies identification of expected sunk costs of offering a product in a market.

Research on Game Virtual Character Behavior Decision Driven by Artificial Intelligence Algorithms

Online games have achieved sustained and rapid growth in various aspects such as game variety, quantity, genre, market size, and number of practitioners. The significance of behavioral Decision model is to make virtual roles have the ability to be as similar as possible to the behavior of real entity objects. Therefore, this article attempts to conduct research on AI (Artificial Intelligence) algorithmic driven game virtual character behavior decision-making. Research has shown that the algorithm proposed in this paper can train states in a stable state space faster in behavioral decision-making experiments. In the early learning stage, the advantages of the algorithm are not obvious. With the progress of learning, the Rate of convergence of the algorithm will gradually accelerate. On the whole, the Rate of convergence of the algorithm in this paper is higher than that of ACA (Ant Colony Algorithm) algorithm. This article takes virtual football competition as an example to construct a virtual football competition scene and conduct research on the behavior decision-making module of virtual players participating in football competition. The aim is to make virtual players have a certain degree of autonomy and intelligence, and their behavior decision-making module has a good sense of reality.

Long-term compensation and risk taking: exploring the role of crisis in CEOs' behavior toward mergers and acquisitions

PurposeCorporate governance scholars have built on agency theory premises to document chief executive officers' (CEOs’) debt-based compensation, also known as inside debt, as an effective tool to control excessive risk and deter risky corporate strategies. In this study, the authors draw on behavioral agency model to put these well-established assumptions to the test in a different setting and argue for the context-specific effects of CEOs' long-term compensation.Design/methodology/approachFocusing on corporate mergers and acquisitions in a post-crisis period (2011–2017), the authors cast doubt on agency theory predictions on debt-like compensation, point to the more realistic assumptions of behavioral decision models, and call for more contingency approaches in theoretical arguments.FindingsAn analysis of more than 4000 observations reveals that neither CEOs nor shareholders react significantly to inside debt after the economy recovers. Firm risk is also influenced only marginally by long-term compensation in a normal period of time.Originality/valueWhile extant literature is rather unanimous on risk-reducing impact of inside debt, the study periods span the financial crisis of 2007. This research is the first conducted in regular times to demonstrate that previous findings are biased and heavily influenced by an exogenous shock.

Coevolution modeling of group behavior and opinion based on public opinion perception

It is important to study the evolution mechanism of group behavior for the prevention and control of harmful group behavior on social networks. Most researchers focus on the study of group behavior evolution in a deterministic environment, ignoring the influence of the uncertain environment formed by the randomness of behavioral decision-making, the incompleteness of public opinion information, and the instability of behavioral states. Therefore, this paper starts with the behavior decision-making process of individuals in an uncertain environment, defines the perception of public opinion, and builds a coevolution model of group behavior and opinion. First, considering the impact of public opinion information on group behavior evolution, this paper defines emotional resonance and topic popularity to quantify public opinion perception and designs an uncertain behavior decision-making model based on public opinion perception. Then, for the influence of the instability of the behavioral state on the opinion evolution process, this paper designs an opinion evolution model for perception based on the unstable behavioral state. Finally, we design the behavior state evolution mechanism based on the instability of the behavior state and fuse it with the behavior decision-making model and opinion evolution model to construct a coevolution model of group behavior and opinion. Compared with the experimental results of other models and real group behavior evolution, our model is effective.

Decision-Making Model of Autonomous Driving at Intersection Based on Unified Driving Operational Risk Field

Safety and comfort are the two major requirements for the successful implementation of self-driving cars, which are anticipated to constitute the future generation of transportation. To create safe and effective self-driving car trajectories, a novel behavioral decision model is developed. First, a risk field model for driving activities based on vehicle kinematics and Eulerian solenoids is constructed. From there, the principle of least action is applied to produce the best trajectory points. Finally, nine typical unit scenarios are simulated by matlab’s driving scenario designer to verify the feasibility of the decision-making algorithm. This study illustrates how an unified operational risk field can efficiently increase intersection passing efficiency while ensuring safety, utilizing the principle of least action. The experimental results show that in the scenario of unprotected left turn and more than 5 vehicles in the intersection, the decision-making model improves the pass rate by 23% compared with the TTI (Time To Intersection) threshold method.

Generalized Behavior Decision-Making Model for Ship Collision Avoidance via Reinforcement Learning Method

Due to the increasing number of transportation vessels, marine traffic has become more congested. According to the statistics, 89% to 95% of maritime accidents are related to human factors. In order to reduce marine incidents, ship automatic collision avoidance has become one of the most important research issues in the field of ocean engineering. A generalized behavior decision-making (GBDM) model, trained via a reinforcement learning (RL) algorithm, is proposed in this paper, and it can be used for ship autonomous driving in multi-ship encounter situations. Firstly, the obstacle zone by target (OZT) is used to calculate the area of future collisions based on the dynamic information of ships. Meanwhile, a virtual sensor called a grid sensor is taken as the input of the observation state. Then, International Regulations for Preventing Collision at Sea (COLREGs) is introduced into the reward function to make the decision-making fully comply with COLREGs. Different from the previous RL-based collision avoidance model, the interaction between the ship and the environment only works in the collision avoidance decision-making stage. Finally, 60 complex multi-ship encounter scenarios clustered by the COLREGs are taken as the ship’s GBDM model training environments. The simulation results show that the proposed GBDM model and training method has flexible scalability in solving the multi-ship collision avoidance problem complying with COLREGs in different scenarios.

Algorithm and Examples of an Agent-Based Evacuation Model

This research establishes a “detect-decide-action” agent-based evacuation model based on the social force model, introducing an active steering force into the basis of the dynamic equation with the combination of the behavioral decision model and the probability model. In the AEM, the detection algorithm is used to identify pedestrians or obstacles within the detection radius to provide the next walking direction and apply the active steering force. The obstacle avoidance algorithm is the core of the “action” link. This research focuses on the establishment of the following and bypassing algorithm when moving in the same direction, and the algorithm of a detour when moving in the opposite direction, applying C++ programming language to achieve the basic evacuation behavior simulation of avoiding pedestrians and obstacles in the actual scene. The results show that compared with the grid model and the general social force model, the agent model (AEM) solves the problem of the distortion of evacuation behavior to some extent, and the pedestrian is more flexible in the choice of evacuation path.

Modeling and Simulation for Non-Motorized Vehicle Flow on Road Based on Modified Social Force Model

Non-motorized vehicles have become one of the most commonly used means of transportation for people due to their advantages of low carbon, environmental protection, convenience and safety. Frequent interaction among non-motorized vehicle users in the shared space will bring security risks to their movement. Therefore, it is necessary to adopt appropriate means to evaluate the traffic efficiency and safety of non-motorized vehicle users in the passage, and using a micro model to conduct simulation evaluation is one of the effective methods. However, some existing micro simulation models oversimplify the behavior of non-motorized vehicle users, and cannot reproduce the dynamic interaction process between them. This paper proposes a modified social force model to simulate the dynamic interaction behaviors between non-motorized vehicle users on the road. Based on the social force model, a new behavioral force is introduced to reflect the three dynamic interaction behaviors of non motor vehicle users, namely, free movement, following and overtaking. Non-motorized vehicle users choose which behavior is determined by the introduced decision model. In this way, the rule-based behavior decision model is combined with the force based method to simulate the movement of non-motorized vehicles on the road. The modified model is calibrated using 1534 non-motorized vehicle trajectories collected from a road in Xi’an, Shaanxi, China. The validity of the model is verified by analyzing the speed distribution and decision-making process of non-motorized vehicles, and comparing the simulation results of different models. The effects of the number of bicycles and the speed of electric vehicles on the flow of non-motorized vehicles are simulated and analyzed by using the calibrated model. The relevant results can provide a basis for urban management and road design.

Inside Debt and Risk: Is There A Link?

The disclosure of CEOs' pension benefits and deferred compensation in 2007 provided scholars with the opportunity to test agency theory predictions on these long-term compensations, also known as inside debt. While the studies showed this form of payment as an effective tool in controlling excessive risk, they all overlapped with the financial crisis of 2007 and its aftershocks. Drawing on behavioral agency model and focusing on corporate mergers and acquisitions as a high-risk investment strategy, we demonstrate that neither CEOs nor shareholders remain sensitive to inside debt after the economy recovers. Our analysis of nearly 4000 observations in a post-crisis period (2011-2017) reveals that the previous findings are heavily influenced by the collapse of financial markets. Disproving agency theory arguments on debt-like compensation, our results point to the more realistic assumptions of behavioral decision models and call for more contingency approaches to theoretical inference. Practical implications are also discussed for designing executives’ compensation packages as a means of mitigating the next potential crises.

Can Livestock Raising Alleviate Farmland Abandonment?—Evidence from China

Farmland abandonment is a global phenomenon. Changes in socioeconomic factors in China impact the traditional crop–livestock system; however, studies on the relationship between livestock raising and farmland abandonment are insufficient. This study used the farmer behavior decision-making model to analyze the impact of livestock raising on farmland abandonment and its mechanism. Based on 6707 samples from the 2016 database of the China Labor-force Dynamic Survey, the Logit and Tobit models were used to empirically analyze the relationship between livestock raising and farmland abandonment at the national level and different terrains (plain, hill and mountain) in China. The results showed farmland abandonment in 15.63% of rural households, and a farmland abandonment ratio of 6.24%. The spatial distribution of farmland abandonment was high in the south and low in the north. Livestock raising households accounted for 9.45%, and the influence coefficient of livestock raising on farmland abandonment was negative but not significant. Livestock raising would significantly increase the ratio of rural households with farmland abandonment by 3.9% and 10% in plain areas and hilly areas, respectively, and decrease the ratio in mountain areas by 11.4%. The abandonment ratio due to livestock raising increased by 21.46% in hilly areas and decrease by 41% in mountain areas. For every 1% increase in livestock scale, the ratio of households with farmland abandonment in plain and hilly areas increased by 0.05% and 0.07%, respectively, and in mountain areas decreased by 0.09%. The abandonment ratio in hilly areas increased by 0.02% and in mountain areas decreased by 0.05%. The effects of raising livestock on farmland abandonment differed across terrains and thus require different measures for alleviating. Plain areas and hilly areas could combine livestock raising and crop planting between different households to improve farmland production capacity, and mountain areas could moderately develop livestock raising to alleviate farmland abandonment.

How Does Past Performance of Competitors Influence Designers’ Cognition, Behaviors, and Outcomes?

Abstract Existing literature on information sharing in contests has established that sharing contest-specific information influences contestant behaviors, and thereby, the outcomes of a contest. However, in the context of engineering design contests, there is a gap in knowledge about how contest-specific information such as competitors’ historical performance influences designers’ actions and the resulting design outcomes. To address this gap, the objective of this study is to quantify the influence of information about competitors’ past performance on designers’ belief about the outcomes of a contest, which influences their design decisions, and the resulting design outcomes. We focus on a single-stage design competition where an objective figure of merit is available to the contestants for assessing the performance of their design. Our approach includes (i) developing a behavioral model of sequential decision making that accounts for information about competitors’ historical performance and (ii) using the model in conjunction with a human-subject experiment where participants make design decisions given controlled strong or weak performance records of past competitors. Our results indicate that participants spend greater efforts when they know that the contest history reflects that past competitors had a strong performance record than when it reflects a weak performance record. Moreover, we quantify cognitive underpinnings of such informational influence via our model parameters. Based on the parametric inferences about participants’ cognition, we suggest that contest designers are better off not providing historical performance records if past contest outcomes do not match their expectations setup for a given design contest.

User Experience Evaluation of B2C E-Commerce Websites Based on Fuzzy Information

With the popularization of personal computers and the development of the Internet, the number of netizens is increasing. The emerging B2C e-commerce platform shows the fierce competition in the e-commerce market. B2C e-commerce distribution is faced with the problems of high distribution cost, long time, and poor quality, which leads to the poor user experience of B2C online shopping and the lack of trust in e-commerce enterprises. This research mainly discusses the user experience evaluation of B2C e-commerce websites based on fuzzy information. First, the AHP analytic hierarchy process is used to construct a hierarchical evaluation system, and then, the two-by-two judgment matrix is compared to the index factors at all levels, and experts are invited to score the method to determine the weight of each index to test whether it meets the consistency requirements. Finally, the fuzzy comprehensive evaluation method is used to perform fuzzy conversion of the original weight, and the membership degree set of the user experience evaluation factors is given, and the fuzzy comprehensive calculation result of the B2C website performance level is calculated. By combining in-depth interviews with website users and questionnaire surveys to analyze the behavioral characteristics of user information navigation, summarize the demand list for product search, product selection, product comparison, and product detail page browsing, and provide a reference for the design and development of information navigation. Calculating the decision model, mainly using fuzzy calculation, calculate the foreground value under each attribute and get the comprehensive foreground value. By comparing with the decision-making behavior model constructed by expected utility theory, it is found that the behavioral decision-making model constructed in this paper based on prospect theory can be closer to the actual situation. In this study, the satisfaction with the function provided reached 68 points, the emotional response 66 points, the aesthetic response 70 points, and the information construction 64 points. Through empirical research, the key factors affecting user satisfaction of B2C e-commerce logistics distribution are summarized, and a B2C e-commerce logistics distribution evaluation system based on user experience is established. This research will provide methods and ideas for the research on user experience design of e-commerce websites and the research and development of related network products. The article helps to draw out the countermeasures and suggestions for the development of the current B2C e-commerce logistics distribution.

Behavior Decision Model With Situation Assessment for Intelligent Vehicles Based on Vehicle-to-Everything Information

Under the application of cooperative vehicle infrastructure technology, vehicles can drive more safely and efficiently in urban traffic environments. This paper presents a behavior decision model composed of a situation assessment module and decision process module to make a decision on lane selection and velocity control. Different common traffic elements are considered, such as surrounding obstacles, traffic lights, lane lines, and road velocity limits, to ensure the model outputs safe and legal behaviors. A green-wave passage strategy is also proposed to allow vehicles to pass through traffic intersections at a smoother velocity and to improve driving efficiency. To verify the correctness and rationality of the model, a Prescan/Simulink co-simulation platform including multiple common urban driving scenarios is established. The results show that the model can output correct behaviors with safety and legitimacy in different scenarios and realize vehicles meeting at intersections, vehicle-following, queuing at intersections with traffic lights, lane-changing for obstacle avoidance, and so forth. Compared with the general decision model without the green-wave passage strategy, the driving time is reduced by an average of 8.53%, and the standard deviation of the driving velocity is reduced by an average of 65.42%, which means that the model can reduce the variation of velocity and improve driving efficiency. The model has a certain practical application value.

A novel behavioral three-way decision model with application to the treatment of mild symptoms of COVID-19

The Coronavirus Disease 2019 (COVID-19) has popularized since late December 2019. In present, it is still highly transmissible and has severe impact on the public health and global economy. Due to the lack of specific drug and the appearance of different variants, the selection of the antiviral therapy to treat the patients with mild symptom is of vital importance. Hence, in this paper, we propose a novel behavioral Three-Way Decision (3WD) model and apply it to the medicine selection decision. First, a new relative utility function is constructed by considering the risk-aversion behavior and regret-aversion behavior of human beings. Second, based on the relative utility function, some new rules are defined to calculate the thresholds and conditional probabilities in 3WD and some corresponding theorems are explored and proved. Next, a new information fusion mechanism in the framework of evidential reasoning algorithm is developed. Then, the decision results are obtained based on the Bayesian decision procedure and the principle of maximum utility. Finally, an example with large-scale data set and an example about medicine selection for COVID-19 are provided to show the implementation process and effectiveness of the proposed method. Comparative analysis and sensitivity analysis are also performed to illustrate the superiority and the robustness of the current proposal.