Behavior Of Stochastic Models Research Articles

Stochastic behavioral models of bedroom window operation in sub-tropical residential buildings

This study investigates window opening and closing behaviors in residential bedrooms in Hong Kong by examining occupants' habits regarding window operation, window opening and closing behavior, and the influence of environmental and contextual factors. Using self-reported survey data on window opening and closing behavior and outdoor environmental data from an open-source database, univariable and multivariable logistic mixed-effect models were developed to evaluate the impact of various parameters. The findings indicate that outdoor environmental factors have a limited influence on window opening and closing probabilities. Conversely, time of the day was identified as a significant factor, emphasizing the role of occupant habits and preferences in determining these probabilities. Importantly, the study results agreed with the observations that the drivers between window opening and window closing differed, further emphasizing the need for separate modeling. The study highlights the importance of considering environmental and contextual factors when predicting window operation behaviors. This study contributes to our understanding of building occupant behavior and provides valuable insights for building designers and engineers to enhance energy simulations and optimize building performance.

Ethical Considerations in AI Simulations for Designing Assistive Technologies

Current ethical debates on the use of artificial intelligence (AI) in healthcare approach AI technology in three primary ways. First, they assess the risks and potential benefits of current AI-enabled products using ethical checklists. Second, they propose ex ante lists of ethical values relevant to the design and development of assistive technologies. Third, they advocate for incorporating moral reasoning into AI's automation processes. These three perspectives dominate the discourse, as evidenced by a brief literature summary. We propose a fourth approach: viewing AI as a methodological tool to aid ethical reflection. This involves an AI simulation concept informed by three elements: 1) stochastic human behavior models based on behavioral data for simulating realistic scenarios, 2) qualitative empirical data on value statements regarding internal policy, and 3) visualization components to illustrate the impact of variable changes. This approach aims to inform an interdisciplinary field about anticipated ethical challenges or trade-offs in specific settings, prompting a re-evaluation of design and implementation plans. This is particularly useful for applications involving complex values and behaviors or limited communication resources, such as dementia care or care for individuals with cognitive impairments. While simulation does not replace ethical reflection, it allows for detailed, context-sensitive analysis during the design process and before implementation.Finally, we discuss the quantitative analysis methods enabled by stochastic simulations and the potential for these simulations to enhance traditional thought experiments and future-oriented technology assessments.

Instant Inverse Modeling of Stochastic Driving Behavior With Deep Reinforcement Learning

Instant Inverse Modeling of Stochastic Driving Behavior With Deep Reinforcement Learning

Enhancing HVAC energy management through multi-zone occupant-centric approach: A multi-agent deep reinforcement learning solution

Occupant-centric HVAC control places a premium on factors including thermal comfort and electricity cost to guarantee occupant satisfaction. Traditional approaches, reliant on static models for occupant behaviors, fall short in capturing intra-day behavioral variations, resulting in imprecise thermal comfort evaluations and suboptimal HVAC energy management, especially in multi-zone systems with diverse occupant profiles. To address this issue, this paper proposes a novel occupant-centric multi-zone HVAC control approach that intelligently schedules cooling and heating setpoints using Multi-agent Deep Reinforcement Learning (MADRL). This approach systematically takes into account stochastic occupant behavior models, such as dynamic clothing insulation adjustments, metabolic rates, and occupancy patterns. Simulation results demonstrate the efficacy of the proposed approach. Comparative case studies show that the proposed MADRL-based, occupant-centric HVAC control reduces electricity costs by 51.09% compared to rule-based approaches and 4.34% compared to single-agent DRL while maintaining multi-zonal thermal comfort for occupants.

AI-assisted ethics? considerations of AI simulation for the ethical assessment and design of assistive technologies.

Current ethical debates on the use of artificial intelligence (AI) in healthcare treat AI as a product of technology in three ways. First, by assessing risks and potential benefits of currently developed AI-enabled products with ethical checklists; second, by proposing ex ante lists of ethical values seen as relevant for the design and development of assistive technology, and third, by promoting AI technology to use moral reasoning as part of the automation process. The dominance of these three perspectives in the discourse is demonstrated by a brief summary of the literature. Subsequently, we propose a fourth approach to AI, namely, as a methodological tool to assist ethical reflection. We provide a concept of an AI-simulation informed by three separate elements: 1) stochastic human behavior models based on behavioral data for simulating realistic settings, 2) qualitative empirical data on value statements regarding internal policy, and 3) visualization components that aid in understanding the impact of changes in these variables. The potential of this approach is to inform an interdisciplinary field about anticipated ethical challenges or ethical trade-offs in concrete settings and, hence, to spark a re-evaluation of design and implementation plans. This may be particularly useful for applications that deal with extremely complex values and behavior or with limitations on the communication resources of affected persons (e.g., persons with dementia care or for care of persons with cognitive impairment). Simulation does not replace ethical reflection but does allow for detailed, context-sensitive analysis during the design process and prior to implementation. Finally, we discuss the inherently quantitative methods of analysis afforded by stochastic simulations as well as the potential for ethical discussions and how simulations with AI can improve traditional forms of thought experiments and future-oriented technology assessment.

Many-objective day-ahead optimal scheduling of residential flexible loads integrated with stochastic occupant behavior models

Residential flexible load scheduling is playing a more and more vital role in balancing the supply and demand of utility grids with the increasing penetration of solar and wind power generation. Nevertheless, the optimal scheduling of residential flexible loads is significantly impacted by occupant behaviors of household appliance usage. In previous studies, occupant behaviors were generally oversimplified with pre-defined schedules, which ultimately renders optimal dispatch plans infeasible. To address this issue, a new framework has been proposed to optimize the day-ahead scheduling of residential flexible loads, which considers the stochastic occupant behaviors. Firstly, the feature parameters to characterize occupant behaviors of thermostatically controlled loads and deferrable loads were proposed, considering the operation characteristics and scheduling optimization of these flexible loads. A general and easy-to-use stochastic occupant behavior model was then developed using Metropolis Hasting algorithm-based Markov chain Monte Carlo method. The feature parameters derived from the validated stochastic occupant behavior model were then input into the established many-objective optimal model to perform day-ahead optimal dispatch, considering the benefits of various stakeholders. NSGA-III and TOPSIS methods were employed to solve the optimal model. The proposed framework was found to be feasible, as the daily electricity costs, the daily CO2 emissions, and the average ramping index of household power profiles were all decreased by 6.25 %, 6.3 %, and 15.0 %, respectively, when compared to the benchmark. Overall, this framework provides practical guidance for more accurate and reliable day-ahead optimal scheduling of residential flexible loads.

Energy Performance of Occupant Behaviors on Windows: A Green Building Based Study

In this paper, the window-opening behavior in a three-star green building with an operation mark in Ningbo was investigated. Four single offices facing south were selected, and the measurement lasted 20–40 days in the cooling season. An analysis of the relationship between window use and environmental factors was assessed by monitoring the occupancy state, the window action and window state, the time, and the temperature and humidity. A statistical analysis method was conducted to reveal the similarities and differences in window use among different occupants. The main findings were as follows: The window adjustment behaviors of different people vary significantly. In terms of adjustment time, there are two modes of behaviors. In addition, the occupancy state had a great impact on window action. Moreover, some people prefer to open the windows in the morning and close the windows at departure time, However, some of them prefer to open the windows at departure time and close the windows at the arrival time. Some irrational window opening behaviors were found in this research. Through correlation analysis, it was shown that the longer time the occupants were in the office, the higher window-opening duration and window-opening frequency would be. If the window was closed at arrival time, the total opening duration of window was significantly reduced. Also, window opening duration and closing duration are proportional to each other. With the help of principal component analysis (PCA) of the 12 features that affected window adjustment, the correlation between the window opening behaviors and the features could be clearly illustrated. A stochastic model of window-opening behavior based on logistics regression and uncertainty analysis was used in this paper. The cooling energy consumption of the stochastic model is 99.9% higher than the energy consumption generated by a fixed number of air changes, and the average energy consumption exceeds the fixed value by more than 21%. The findings of this study have certain guiding significance for promoting energy conservation by occupants’ behaviors in green buildings in this region.

Distributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon

Human-leading truck platooning systems have been proposed to leverage the benefits of both human supervision and vehicle autonomy. Equipped with human guidance and autonomous technology, human-leading truck platooning systems are more versatile to handle uncertain traffic conditions than fully automated platooning systems. This paper presents a novel distributed stochastic model predictive control (DSMPC) design for a human-leading heavy-duty truck platoon. The proposed DSMPC design integrates the stochastic driver behavior model of the human-driven leader truck with a distributed formation control design for the following automated trucks in the platoon. The driver behavior of the human-driven leader truck is learned by a stochastic inverse reinforcement learning (SIRL) approach. The proposed stochastic driver behavior model aims to learn a distribution of cost function, which represents the richness and uniqueness of human driver behaviors, with a given set of driver-specific demonstrations. The distributed formation control includes a serial DSMPC with guaranteed recursive feasibility, closed-loop chance constraint satisfaction, and string stability. Simulation studies are conducted to investigate the efficacy of the proposed design under several realistic traffic scenarios. Compared to the baseline platoon control strategy (deterministic distributed model predictive control), the proposed DSMPC achieves superior controller performance in constraint violations and spacing errors.

Impacts of Occupant Behavior on Building Energy Consumption and Energy Savings Analysis of Upgrading ASHRAE 90.1 Energy Efficiency Standards

Commercial prototype building models were developed by the United States Department of Energy (DOE) to analyze the energy savings of the ASHRAE 90.1 standard. However, in the DOE models, occupant behavior inputs were deterministic and the stochasticity of occupant behavior was not fully characterized. This study evaluated the impacts of stochastic occupant behavior on building energy consumption and energy savings analysis from upgrading the ASHRAE 90.1-2016 to 2019 in sixteen climate zones in the United States (U.S.). Three occupant behavior styles (austerity, normal, and wasteful) were defined to represent the different levels of energy consciousness. The DOE medium office prototype models were used as the baseline (normal behavior style). The occupant behavior functional mock-up unit (obFMU) was used to model the stochastic occupant behavior models (austerity and wasteful). The EnergyPlus medium office prototype models were modified to co-simulate with the obFMU models. The results of 16 climate zones were aggregated by the relative construction volume of each climate zone. The results showed that the weighted national annual source energy use intensity (EUI) of the austerity, normal, and wasteful behavior styles were 203.81 kWh/m2, 287.15 kWh/m2, and 388.33 kWh/m2 for ASHRAE 90.1-2016, and 192.43 kWh/m2, 273.48 kWh/m2, and 371.28 kWh/m2 for ASHRAE 90.1-2019, respectively. Compared to the normal behavior style, the austerity behavior style consumed 29% less source energy, while the wasteful behavior style consumed 36% more source energy. From upgrading the ASHRAE 90.1-2016 to 2019, the energy saving percentages based on the austerity, normal, and wasteful behavior styles were 5.59%, 4.78%, and 4.42%, respectively. The stochastic occupant behavior significantly impacted the building energy consumption, and their impacts on the energy savings analysis of upgrading ASHRAE 90.1 were also not negligible.

Analysis of a Class of Stochastic Animal Behavior Models under Specific Choice Preferences

The behavior of animals can be studied in two ways: experimentally, in labs or in the field, or theoretically, via modeling. Extensive research on animal behavior in probabilistic learning circumstances has produced findings that are consistent with so-called occurrence studies. However, such cases can be classified into four different events, depending on the location of the food and the side chosen by the animals. This article sought to overcome these limitations by offering a generic stochastic model under these conditions that can be utilized to analyze the broad range of models that have been reported in the literature. We explored the existence, uniqueness, and stability results of the proposed model using well-known fixed-point methods. Additionally, we present some examples to highlight the significance of our results.

Exposure Inequality in People Recommender Systems: The Long-Term Effects

People recommender systems may affect the exposure that users receive in social networking platforms, influencing attention dynamics and potentially strengthening pre-existing inequalities that disproportionately affect certain groups. In this paper we introduce a model to simulate the feedback loop created by multiple rounds of interactions between users and a link recommender in a social network. This allows us to study the long-term consequences of those particular recommendation algorithms. Our model is equipped with several parameters to control: (i) the level of homophily in the network, (ii) the relative size of the groups, (iii) the choice among several state-of-the-art link recommenders, and (iv) the choice among three different stochastic user behavior models, that decide which recommendations are accepted or rejected. Our extensive experimentation with the proposed model shows that a minority group, if homophilic enough, can get a disproportionate advantage in exposure from all link recommenders. Instead, when it is heterophilic, it gets underexposed. Moreover, while the homophily level of the minority affects the speed of the growth of the disparate exposure, the relative size of the minority affects the magnitude of the effect. Finally, link recommenders strengthen exposure inequalities at the individual level, exacerbating the“rich-get-richer” effect: this happens for both the minority and the majority class and independently of their level of homophily.

Functional safety analysis of safety-critical system using state transition diagram

The subject of research is to determine the functional safety indicators of a fault-tolerant safety-critical system, namely, the minimal cut sets’ probability for a given duration of the system’s operation, using the state transition diagram (STD). The aim is to create a new method for analyzing the functional safety of a fault-tolerant safety-critical system. This method is based on the methodology of developing models of operational reliability behavior in the form of STD. This methodology provides a detailed representation of inoperable states and their relation with pre-failure (inoperable critical) states. The task is to propose a new classification for inoperable states of the STD to obtain all possible emergencies in the same space of inoperable states. This approach allows consideration the correlations between the failures, that it is impossible to use the fault trees. Since the space of inoperable states can reach hundreds and thousands of states, a method is proposed for their automated determination according to the classification. The state space method was used to conduct the validation of the method of functional safety analysis. The following results were obtained: the system of Chapman-Kolmogorov differential equations is formed in accordance with the STD and it provides the dependence of the functional safety indicator – the minimal cut sets’ probability as a function of the operational duration of the fault-tolerant safety-critical system. This dependence is called the emergency function. The method for determining the emergency function is based on the usage of the emergency mask. Note that the proposed model of operational reliability behavior in the form of STD provides the possibility to conduct both the functional safety and the reliability indicators. The value of the minimal cut sets’ probability for a given duration of operation is determined using the fault tree for the validation of the proposed method of functional safety analysis. The fault tree was built by Reliasoft BlockSim software. The obtained value coincides with the value of the minimal cut sets’ probability, which was defined by the emergency function for the same operational duration. Thus, the designer can comprehensively analyze the feasibility of introducing redundancy (structural, temporal, functional). Conclusions: the scientific novelty of the obtained results is the following: the new method for determining safe, critical and catastrophic states in the set of inoperable states is used in the methodology of the STD developing to obtain the stochastic model of operational reliability behavior of fault-tolerant safety-critical system. This technique ensures an automated defining of emergency function by using an improved structural-automatic model.

A multiscale stochastic criminal behavior model and the convergence to a piecewise-deterministic-Markov-process limit

Ever since the pioneering human–environment interaction model of criminal behavior [M. B. Short, M. R. DOrsogna, V. B. Pasour, G. E. Tita, P. J. Brantingham, A. L. Bertozzi and L. B. Chayes, A statistical model of criminal behavior, Math. Models Methods Appl. Sci. 18 (2008) 1249–1267] was published, many mathematical agent-based residential burglary models have been proposed. In order to reach an improved balance among model accuracy, analysis simplicity and real-world data fitting tractability, we introduce in this paper a multi-scale hybrid interacting-particle-system model of criminal behavior in a discrete setting. We assume that agents’ actions are governed by independent Poisson clocks, while the environment variable evolves on a separate finer discrete spatial-temporal scale. Furthermore, as we refine the second scale to its scaling limit, the hybrid system converges to a piecewise deterministic Markov process (PDMP). Through a martingale approach and infinitesimal generator analysis, we provide a formal derivation for the convergence. Computer simulations of coupled hybrid and PDMP systems both exhibit spatio-temporal aggregates of crime and show excellent agreement between the two, which supports our theoretical derivation of the scaling limit. The methodology and results we develop here indicate a way to establish connections for the proposed crime models.

Multiple epidemic waves as the outcome of stochastic SIR epidemics with behavioral responses: a hybrid modeling approach.

In the behavioral epidemiology (BE) of infectious diseases, little theoretical effort seems to have been devoted to understand the possible effects of individuals’ behavioral responses during an epidemic outbreak in small populations. To fill this gap, here we first build general, behavior implicit, SIR epidemic models including behavioral responses and set them within the framework of nonlinear feedback control theory. Second, we provide a thorough investigation of the effects of different types of agents’ behavioral responses for the dynamics of hybrid stochastic SIR outbreak models. In the proposed model, the stochastic discrete dynamics of infection spread is combined with a continuous model describing the agents’ delayed behavioral response. The delay reflects the memory mechanisms with which individuals enact protective behavior based on past data on the epidemic course. This results in a stochastic hybrid system with time-varying transition probabilities. To simulate such system, we extend Gillespie’s classic stochastic simulation algorithm by developing analytical formulas valid for our classes of models. The algorithm is used to simulate a number of stochastic behavioral models and to classify the effects of different types of agents’ behavioral responses. In particular this work focuses on the effects of the structure of the response function and of the form of the temporal distribution of such response. Among the various results, we stress the appearance of multiple, stochastic epidemic waves triggered by the delayed behavioral response of individuals.

State Prediction of Human-in-the-Loop Multi-rotor System with Stochastic Human Behavior Model

Reachability analysis is a widely used method to analyze the safety of a Human-in-the-Loop Cyber Physical System (HiLCPS). It allows the HiLCPS to respond against an imminent threat in advance by predicting reachable states of the system. However, it could lead to an unnecessarily conservative reachable set if the prediction only relies on the system dynamics without explicitly considering human behavior, and thus the risk might be overestimated. To avoid the conservativeness, we present a state probability distribution function (pdf) prediction method which takes into account a stochastic human behavior model represented as a Gaussian Mixture Model (GMM). In this paper, we focus on the multi-rotor controlled by a human operator in a near-collision situation. The stochastic human behavior model is trained using experimental data to represent the human operators’ evasive maneuver. Then, we can retrieve a human control input pdf from the trained stochastic human behavior model using the Gaussian Mixture Regression (GMR). The proposed algorithm predicts the multi-rotor's future state pdf by propagating the pdf of the retrieved human control input according to the given dynamics, which yields closed-loop analysis of the HiLCPS. Human subject experiment results are provided to demonstrate the effectiveness of the proposed algorithm.

Inverse Resource Rational Based Stochastic Driver Behavior Model

Human drivers have limited and time-varying cognitive resources when making decisions in real-world traffic scenarios, which often leads to unique and stochastic behaviors that can not be explained by perfect rationality assumption, a widely accepted premise in modeling driving behaviors that presume drivers rationally make decisions to maximize their own rewards under all circumstances. To explicitly address this disadvantage, this study presents a novel driver behavior model that aims to capture the resource rationality and stochasticity of the human driver's behaviors in realistic longitudinal driving scenarios. The resource rationality principle can provide a theoretic framework to better understand the human cognition processes by modeling human's internal cognitive mechanisms as utility maximization subject to cognitive resource limitations, which can be represented as finite and time-varying preview horizons in the context of driving. An inverse resource rational-based stochastic inverse reinforcement learning approach (IRR-SIRL) is proposed to learn a distribution of the planning horizon and cost function of the human driver with a given series of human demonstrations. A nonlinear model predictive control (NMPC) with a time-varying horizon approach is used to generate driver-specific trajectories by using the learned distributions of the planning horizon and the cost function of the driver. The simulation experiments are carried out using human demonstrations gathered from the driver-in-the-loop driving simulator. The results reveal that the proposed inverse resource rational-based stochastic driver model can address the resource rationality and stochasticity of human driving behaviors in a variety of realistic longitudinal driving scenarios.

Impact of Window-to-Wall Ratio on Heating Demand and Thermal Comfort When Considering a Variety of Occupant Behavior Profiles

Energy consumption and thermal comfort in residential buildings are highly influenced by occupant behavior, which exhibits a high level of day-to-day and dwelling-to-dwelling variance. Although occupant behavior stochastic models have been developed in the past, the analysis or selection of a building design parameter is typically based on simulations that use a single “average” occupant behavior schedule which does not account for all possible profiles. The objective of this study is to enhance the understanding of how window-to-wall ratio (WWR) of a residential unit affects heating demand and thermal comfort when considering occupant behavior diversity through a parametric analysis. To do so, a stochastic occupant behavior model generates a high number of possible profiles, which are then used as input in an energy simulation of the dwelling. As a result, one obtains probability distributions of energy consumption and comfort for different WWR values. The paper shows that the shape of the probability distributions is affected by WWR and dwelling orientation, and that the influence of different occupant behavior aspects on performance also varies with WWR. This work could help designers to better assess the impact of WWR for a large spectrum of possible occupant behavior profiles.

A stochastic model of the security system's functional behavior for a critical infrastructure object

Context. Objects of critical infrastructure require adequate security. The security systems for such objects have a specific requirements. Fulfilment of part of these requirements can provide by an unattended ground sensor complex which uses seismic sensors. The second part of the requirements can be performed by an unmanned aircraft complex. Therefore, it is seen as practical feasibility to combine these two complexes in one security system with three control zones. The first (Further) and second (Hither) control zones are served by an unattended ground sensor complex. The third control zone (intruder escort zone) is assigned on the data from the unattended ground sensor complex: the type of intruder, his speed and direction of movement. The unmanned aircraft complex is used to perform a security system task in the intruder's tracking area on command from the unattended ground sensor complex. You need to know two question answers to implement such a security system. First, what is the efficiency indicator value of the security system will ensure the combination of the existing complexes with their functionality indicators. And the second, what should be the functionality indicators requirements of these complexes in order for the security system to provide the required performance indicator value. Objective. Therefore, the goal of the article is to show the potential value of the efficiency index of the security system formed from the existing complexes, as well as to assess the possibility of increasing its efficiency. Methods. This goal is achieved by the stochastic model of functional behavior, which should reproduce all variants of the security system components reaction to the crossing of three control zones by intruders. Results. The main result is a discrete-continuous stochastic model of the functional behavior of the critical infrastructure object security system in the form of 76 Kolmogorov-Chapman differential equations system. The grade of the stochastic model adequacy makes it possible, in addition to the analysis tasks of the security system effectiveness, to solve the synthesis tasks of functionality indicators of the complex components that are integrated into the security system. Conclusions. Research was conducted have shown that it is impractical to create a security system based on existing unattended ground sensor complexes and unmanned aircraft systems for critical infrastructure objects because of the unacceptably low system efficiency. Based on the synthesis task solution, the functionality indicators values of the each complex components, in particular those that will provide the required value of the security system efficiency indicator, are presented. The stochastic model of the functional behavior of the security system can be used in the information technology of its design to find compromise solutions between different variants of composition, structure and functional behavior of the security system.

Stochastic behavioral models for system level reliability analysis including non-normal and correlated process variation

The behavior of modern integrated circuits is altered by numerous effects such as process variation, voltage or temperature shifts and aging (PVTA). To evaluate the impact on system level, extensive simulations must be carried out on transistor-level which is not applicable for larger systems. This paper presents methods for the generation of stochastic behavioral models (SBMs) of circuit components to enable the reliability analysis of individual performances on system level. Different modeling approaches are presented, based on either Response Surface Models for parametric or Kernel Density Estimation for non-normal performance distributions. Additionally, an approach to incorporate the correlation between individual system components is analyzed. The SBMs are evaluated on analog example circuits consisting of several components.

Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning

Drivers have unique and rich driving behaviors when operating vehicles in traffic. This paper presents a novel driver behavior learning approach that captures the uniqueness and richness of human driver behavior in realistic driving scenarios. A stochastic inverse reinforcement learning (SIRL) approach is proposed to learn a distribution of cost function, which represents the richness of the human driver behavior with a given set of driver-specific demonstrations. Evaluations are conducted on the realistic driving data collected from the 3D driver-in-the-loop driving simulation. The results show that the learned stochastic driver model is capable of expressing the richness of the human driving strategies under different realistic driving scenarios. Compared to the deterministic baseline driver behavior model, the results reveal that the proposed stochastic driver behavior model can better replicate the driver's unique and rich driving strategies in a variety of traffic conditions.