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Journal articles on the topic 'Behavior planning'

To see the other types of publications on this topic, follow the link: Behavior planning.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Rymon, R., B. L. Webber, and J. R. Clarke. "Progressive horizon planning-planning exploratory-corrective behavior." IEEE Transactions on Systems, Man, and Cybernetics 23, no. 6 (1993): 1551–60. http://dx.doi.org/10.1109/21.257753.

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2

Twardy, Bernadette M., and Beverly J. Yerg. "The Impact of Planning on Inclass Interactive Behaviors of Preservice Teachers." Journal of Teaching in Physical Education 6, no. 2 (January 1987): 136–48. http://dx.doi.org/10.1123/jtpe.6.2.136.

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This study explored the relationships between teacher planning behaviors and the inclass behaviors of teachers and learners in a 30-min lesson on the volleyball spike. All 30 teacher subjects progressed through three consecutive stages: 30-min planning phase, 30-min instructional phase, and a brief self-report phase. During the planning session, subjects were instructed to plan their lesson by utilizing the talk aloud technique. Planning data were coded through the use of planning indicators obtained from the Florida Performance Measurement System. Immediately after the planning phase each subject implemented his or her lesson. Teacher and learner behavior was live-coded by three trained observers using Birdwell’s Academic Learning Time-Physical Education-Teacher Behavior Observation System (ALT-PE-TB). Frequencies of teacher planning behavior were compared with the frequencies of inclass teaching behavior and learner behavior. The results indicated that significant relationships did exist between certain planning behaviors and the inclass behavior of teachers and learners.

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3

Noh, Su-Hee, and Yong-Tae Kim. "Behavior Planning for Humanoid Robot Using Behavior Primitive." Journal of Korean Institute of Intelligent Systems 19, no. 1 (February 25, 2009): 108–14. http://dx.doi.org/10.5391/jkiis.2009.19.1.108.

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4

Ali, Saleem H. "Environmental Planning and Cooperative Behavior." Journal of Planning Education and Research 23, no. 2 (December 2003): 165–76. http://dx.doi.org/10.1177/0739456x03256247.

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5

Leinsdorff, Torben. "Buying behavior and product planning." International Journal of Production Economics 41, no. 1-3 (October 1995): 237–39. http://dx.doi.org/10.1016/0925-5273(95)00068-2.

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6

Liu, Yong, Vassilis Kostakos, and Hongxiu Li. "Climatic Effects on Planning Behavior." PLOS ONE 10, no. 5 (May 19, 2015): e0126205. http://dx.doi.org/10.1371/journal.pone.0126205.

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7

Ahmad, Jamaluddin. "The Behavior Of Bureaucracy in Formulation Annual Government Planning Process." Journal of Public Administration and Governance 3, no. 3 (September 1, 2013): 108. http://dx.doi.org/10.5296/jpag.v3i3.4383.

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The consistency of regional government budget is actually reflected from adjustment planning and budgeting program, which is really determined by formulating process of annual government planning every year. Formulating process is bureaucracy responsibility, so most of behaviors of bureaucracy where dominated. This research aimed at: (1) describing behaviors of bureaucracy types in formulating process annual government planning, (2) describing external environment models interaction with behaviors of bureaucracy types in formulating process annual government planning, and (3) explain the formulating process of annual government planning principles with behaviors of bureaucracy types. The results of the research showed that between behaviors of bureaucracy types consists of: career staff type, politics type, professional type, and missioner type, which dominated by the career staff type in formulating process annual government planning. This happens because of the perception, individual decision making, communication patterns, unit leadership, internal organization and culture. While the external environment interaction model of bureaucratic behavior is a social model the determined the regency leadership factor, factor structure, factor of bureaucratic authority, and cultural factors. While the application of the principles annual government planning formulation based on the type of bureaucratic behavior has basically done but still needs improvement.

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8

Ramirez, Miquel, Nitin Yadav, and Sebastian Sardina. "Behavior Composition as Fully Observable Non-Deterministic Planning." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 180–88. http://dx.doi.org/10.1609/icaps.v23i1.13547.

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The behavior composition problem involves the automatic synthesis of a controller able to “realize” (i.e., implement) a target behavior module by suitably coordinating a collection of partially controllable available behaviors. In this paper, we show that the existence of a composition solution amounts to finding a strong cyclic plan for a special non-deterministic planning problem, thus establishing the formal link between the two synthesis tasks. Importantly, our results support the use of non-deterministic planing systemsfor solving composition problems in an off-the-shelf manner. We then empirically evaluate three state-of-the-art synthesis systems (a domain-independent automated planner and two game solvers based on model checking techniques) on various non-trivial composition instances. Our experiments show that while behavior composition is EXPTIME-complete, the current technology is already able to handle instances of significant complexity. Our work is, as far as we know, the first serious experimental work on behavior composition.

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9

Tambovtsev, V. "Planning and opportunism." Voprosy Ekonomiki, no. 1 (January 20, 2017): 22–39. http://dx.doi.org/10.32609/0042-8736-2017-1-22-39.

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The paper analyzes the occurrence of opportunistic behaviour opportunities in the planning processes. The following types of planning are distinguished: self-planning, collective planning, planning on behalf (or by contract), and directive (or prescriptive) planning. It is shown that any type of it excluding self-planning generates incentives to opportunistic behavior. Within this frame-work, two popular approaches in the planning theory are analyzed: participative planning and communicative one; it is shown that they are open to opportunism too. Basing on this analysis, the spheres of economy where government planning can be more efficient than market coordination mechanism are outlined.

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10

Magnaguagno, Maurício C., and Felipe Meneguzzi. "BioPlan: Classical Planning with Crowd simulation." Journal on Interactive Systems 8, no. 1 (September 14, 2017): 1. http://dx.doi.org/10.5753/jis.2017.674.

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Crowd simulation for evacuation situations often assumes that all agents are trying to reach a single point within an environment. Although such an assumption is not entirely wrong, human agents often exhibit more complex behaviors, even if deviations from the standard behavior are not particularly frequent. Classical planning is far from the best way to achieve the minimal path or correct behavior for agents, but adds a deeper level of reasoning about complex goal-achievement and about actions that are more complex than simply moving about. In this paper, we describe a crowd simulation experiment that uses classical AI planning to enrich the behavior of the agents in the scenario. Using this approach, we can express not only the target destination of the agents, but also (sub)goals and path preferences.

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11

Bailey, Ryan R. "Goal Setting and Action Planning for Health Behavior Change." American Journal of Lifestyle Medicine 13, no. 6 (September 13, 2017): 615–18. http://dx.doi.org/10.1177/1559827617729634.

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Health behavior change is challenging for most individuals, but there are many strategies that individuals can use to facilitate their behavior change efforts. Goal setting is one such strategy that assists individuals to identify specific behaviors to change and how to go about doing so. For many, however, simply setting a goal seldom leads to actual behavior change. For some, identifying an appropriate goal is difficult, while for others, putting goals into action is the roadblock. Two strategies may be of assistance for setting and achieving goals. First, consideration of key goal characteristics (eg, approach vs avoidance goals, performance vs mastery goals, level of difficulty) may result in selection of more appropriate and feasible goals. Second, action planning can help individuals put goals into action. Clinicians can help patients utilize these strategies to set and achieve goals for health behavior change.

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12

Zhang, Chun-Qing, Rongyu Fang, Ru Zhang, Martin S. Hagger, and Kyra Hamilton. "Predicting Hand Washing and Sleep Hygiene Behaviors among College Students: Test of an Integrated Social-Cognition Model." International Journal of Environmental Research and Public Health 17, no. 4 (February 13, 2020): 1209. http://dx.doi.org/10.3390/ijerph17041209.

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Objective: Hand washing and sleep hygiene are two important health behaviors. The purpose of the current study was to identify the motivational and volitional antecedents of college students’ hand washing and sleep hygiene behaviors based on an integrated model of behavior that combined social-cognition constructs from the Theory of Planned Behavior (TPB) and Health Action Process Approach (HAPA). Methods: Using a prospective design, college students (N = 1106) completed a survey assessing the motivational constructs of action self-efficacy, attitudes, subjective norm, perceived behavioral control, intentions, and behaviors of hand washing and sleep hygiene at Time 1. Demographic variables were also collected. One month later, at Time 2, college students (N = 524) self-reported on their volitional factors of maintenance self-efficacy, action planning, coping planning, and behaviors of hand washing and sleep hygiene. A further 2 months later, at Time 3, college students (N = 297) were asked to self-report on their hand washing and sleep hygiene behaviors over the past month. Findings: Data were analyzed using variance-based structural equation modelling. Results showed significant direct effects of attitudes, subjective norm, and perceived behavioral control on intentions; significant direct effects of action self-efficacy on maintenance self-efficacy; and significant direct effects of maintenance self-efficacy on action planning and coping planning. Significant direct effects of intention on action planning (sleep hygiene only), and significant direct effects of intention, maintenance self-efficacy (hand washing only), action and coping planning on behavior were also observed. Action planning also moderated the intention–behavior relationship, but only for hand washing. There were also significant total indirect effects of action self-efficacy on behavior mediated by maintenance self-efficacy, action planning, and coping planning for both behaviors, and significant total indirect effects of subjective norm and perceived behavioral control on behavior mediated by intention for sleep hygiene. When past behavior was included in the integrated model predicting all the psychological variables and behavior, all of the structural relations were attenuated. Discussion: Current findings indicate that college students’ hand washing and sleep hygiene behaviors are a function of both motivational and volitional factors. Findings also indicate that the TPB and HAPA pathways might differ for the two health behaviors. Implications of the current findings for future health interventions aimed at improving college students’ hand washing and sleep hygiene are discussed.

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13

Slone, Donald G., and Carey E. Gleason. "Behavior management planning for problem behaviors in dementia: A practical model." Professional Psychology: Research and Practice 30, no. 1 (1999): 27–36. http://dx.doi.org/10.1037/0735-7028.30.1.27.

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14

van Bladel, Kelly, Tom Bellemans, Davy Janssens, and Geert Wets. "Activity Travel Planning and Rescheduling Behavior." Transportation Research Record: Journal of the Transportation Research Board 2134, no. 1 (January 2009): 135–42. http://dx.doi.org/10.3141/2134-16.

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15

KAKIKURA, Masayoshi. "Intelligence in ALV. (1). Behavior planning." Journal of the Robotics Society of Japan 5, no. 5 (1987): 398–402. http://dx.doi.org/10.7210/jrsj.5.398.

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16

Liu, Yong, Vassilis Kostakos, and Hongxiu Li. "Correction: Climatic Effects on Planning Behavior." PLOS ONE 10, no. 6 (June 25, 2015): e0131954. http://dx.doi.org/10.1371/journal.pone.0131954.

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17

Cone, John D. "Intervention Planning using Adaptive Behavior Instruments." Journal of Special Education 21, no. 1 (January 1987): 127–48. http://dx.doi.org/10.1177/002246698702100113.

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18

Brounen, Dirk, Kees G. Koedijk, and Rachel A. J. Pownall. "Household financial planning and savings behavior." Journal of International Money and Finance 69 (December 2016): 95–107. http://dx.doi.org/10.1016/j.jimonfin.2016.06.011.

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19

Gibson, Brian, and Gavin Cassar. "Planning Behavior Variables in Small Firms." Journal of Small Business Management 40, no. 3 (July 2002): 171–86. http://dx.doi.org/10.1111/1540-627x.00049.

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20

Kosaka, Hiroshi. "Japanese managerial behavior in strategic planning." Journal of Business Research 57, no. 3 (March 2004): 291–96. http://dx.doi.org/10.1016/s0148-2963(01)00324-1.

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21

Elder, John P., and Jacqueline D. Estey. "Behavior change strategies for family planning." Social Science & Medicine 35, no. 8 (October 1992): 1065–76. http://dx.doi.org/10.1016/0277-9536(92)90246-m.

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22

Boratto, Ludovico, Matteo Manca, Giuseppe Lugano, and Marián Gogola. "Characterizing user behavior in journey planning." Computing 102, no. 5 (January 29, 2020): 1245–58. http://dx.doi.org/10.1007/s00607-019-00775-8.

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23

Contin-Pilart, Ignacio, Martin Larraza-Kintana, and Victor Martin-Sanchez. "Entrepreneurs’ planning behavior and new firm performance." Management Research: Journal of the Iberoamerican Academy of Management 18, no. 3 (August 31, 2020): 307–34. http://dx.doi.org/10.1108/mrjiam-02-2020-1011.

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Purpose Drawing on institutional logics theory, this paper aims to examine the determinants of entrepreneurs’ planning behavior in the first years of 212 Spanish new firms. Additionally, this study identifies four different planning profiles: systematic planner, early planner, late planner and non-planner. Design/methodology/approach This study’s data structure is a (yearly) pooled cross-sectional time series. This paper investigates the determinants of planning behaviors among entrepreneurs, as well as the impact of that activity on new firm performance (i.e. employment growth). Findings The results confirm the relevance of institutional forces in explaining the involvement of founders of new firms upon planning activities. Institutional factors, in the form of public external support seem to explain early- and systematic-planner behavior while the influence of entrepreneurial family background does so with late-planner behavior. Originality/value The authors focus their attention on two key moments of a new venture’ life: the first year of operation and once the firm has overcome the four-year hurdle that is often used to distinguish new from established businesses. Four different patterns emerge: systematic planner (those who consistently plan over time), early planner (those who engage in planning activities in the early moments of the firm’s life but not later), late planner (those who do not plan at the beginning but end up conducting planning activities a few years later) and non-planner (those who never get involved in planning activities). This new division is an interesting additional feature of this study.

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24

Ishiko, M., T. Kawata, K. Toda, and K. Tomiyama. "The Distributed Behavior Arbitration Network with Integrated Behavior Planning Capability." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2004 (2004): 139. http://dx.doi.org/10.1299/jsmermd.2004.139_2.

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25

Stern, Jenny, Lana Salih Joelsson, Tanja Tydén, Anna Berglund, Maria Ekstrand, Hanne Hegaard, Clara Aarts, Andreas Rosenblad, Margareta Larsson, and Per Kristiansson. "Is pregnancy planning associated with background characteristics and pregnancy‐planning behavior?" Acta Obstetricia et Gynecologica Scandinavica 95, no. 2 (December 8, 2015): 182–89. http://dx.doi.org/10.1111/aogs.12816.

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26

Van Lent, Michael, Mark Riedl, Paul Carpenter, Ryan McAlinden, and Paul Brobst. "Increasing Replayability with Deliberative and Reactive Planning." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 1, no. 1 (September 28, 2021): 135–40. http://dx.doi.org/10.1609/aiide.v1i1.18729.

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Opponent behavior in today's computer games is often the result of a static set of Artificial Intelligence (AI) behaviors or a fixed AI script. While this ensures that the behavior is reasonably intelligent, it also results in very predictable behavior. This can have an impact on the replayability of entertainment-based games and the educational value of training-based games. This paper proposes a move away from static, scripted AI by using a combination of deliberative and reactive planning. The deliberative planning (or Strategic AI) system creates a novel strategy for the AI opponent before each gaming session. The reactive planning (or Tactical AI) system executes this strategy in real-time and adapts to the player and the environment. These two systems, in conjunction with a future automated director module, form the Adaptive Opponent Architecture. This paper describes the architecture and the details of the deliberative and reactive planning components.

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27

Birkenmaier, Julie, and Qiang (John) Fu. "Financial Behavior and Financial Access: A Latent Class Analysis." Journal of Financial Counseling and Planning 31, no. 2 (March 16, 2020): 179–92. http://dx.doi.org/10.1891/jfcp-18-00067.

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This article examined the relationship of household financial behaviors and accesses. Using the 2015 National Financial Capability Study, the current study conducted latent class analysis of financial behaviors to identify latent classes (N = 27,564). The distribution of access was investigated among latent classes, which were regressed on the financial behaviors of financial planning and financial spending factors and other covariates using multinomial logistic regression. After controlling for other variables, the odds of being in Thinly Banked, Limited Access, and Working Families classes instead of being in Investors class decreased by 90%, 88%, and 66% for every point higher in financial planning behavior, respectively. Results suggest that desirable financial behaviors such as planning are important for consumers with the least financial access.

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28

Weijers, Robert, Björn de Koning, Yvonne Vermetten, and Fred Paas. "Nudging Autonomous Learning Behavior: Three Field Experiments." Education Sciences 13, no. 1 (January 1, 2023): 49. http://dx.doi.org/10.3390/educsci13010049.

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Autonomous learning behavior is an important skill for students, but they often do not master it sufficiently. We investigated the potential of nudging as a teaching strategy in tertiary education to support three important autonomous learning behaviors: planning, preparing for class, and asking questions. Nudging is a strategy originating from behavioral economics used to influence behavior by changing the environment, and consists of altering the choice environment to steer human behavior. In this study, three nudges were designed by researchers in co-creation with teachers. A video booth to support planning behavior (n = 95), a checklist to support class preparation (n = 148), and a goal-setting nudge to encourage students to ask questions during class (n = 162) were tested in three field experiments in teachers’ classrooms with students in tertiary education in the Netherlands. A mixed-effects model approach revealed a positive effect of the goal-setting nudge on students’ grades and a marginal positive effect on the number of questions asked by students. Additionally, evidence for increased self-reported planning behavior was found in the video booth group—but no increase in deadlines met. No significant effects were found for the checklist. We conclude that, for some autonomous learning behaviors, primarily asking questions, nudging has potential as an easy, effective teaching strategy.

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29

Tomar, Sweta, H. Kent Baker, Satish Kumar, and Arvid O. I. Hoffmann. "Psychological determinants of retirement financial planning behavior." Journal of Business Research 133 (September 2021): 432–49. http://dx.doi.org/10.1016/j.jbusres.2021.05.007.

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30

Ljaljevic, Agima, Natasa Terzic, Marija Palibrk, and Boban Mugosa. "Behavior of Young People Regarding Family Planning." Materia Socio Medica 25, no. 2 (2013): 93. http://dx.doi.org/10.5455/msm.2013.25.93-95.

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31

Ciolek, Daniel, Nicolás D'Ippolito, Alberto Pozanco, and Sebastian Sardiña. "Multi-Tier Automated Planning for Adaptive Behavior." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 66–74. http://dx.doi.org/10.1609/icaps.v30i1.6646.

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A planning domain, as any model, is never “complete” and inevitably makes assumptions on the environment's dynamic. By allowing the specification of just one domain model, the knowledge engineer is only able to make one set of assumptions, and to specify a single objective-goal. Borrowing from work in Software Engineering, we propose a multi-tier framework for planning that allows the specification of different sets of assumptions, and of different corresponding objectives. The framework aims to support the synthesis of adaptive behavior so as to mitigate the intrinsic risk in any planning modeling task. After defining the multi-tier planning task and its solution concept, we show how to solve problem instances by a succinct compilation to a form of non-deterministic planning. In doing so, our technique justifies the applicability of planning with both fair and unfair actions, and the need for more efforts in developing planning systems supporting dual fairness assumptions.

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32

Kubota, Takashi, Riho Ejiri, and Ichiro Nakatani. "AUTONOMOUS EXPLORATION BEHAVIOR PLANNING FOR PLANETARY ROVER." IFAC Proceedings Volumes 40, no. 15 (2007): 186–91. http://dx.doi.org/10.3182/20070903-3-fr-2921.00034.

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Rogers, Megan L., Anna R. Gai, Amy Lieberman, Katherine Musacchio Schafer, and Thomas E. Joiner. "Why does safety planning prevent suicidal behavior?" Professional Psychology: Research and Practice 53, no. 1 (February 2022): 33–41. http://dx.doi.org/10.1037/pro0000427.

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34

KAWASE, Haruyoshi, Kosuke SEKIYAMA, and Khusniddin FOZILOV. "Real-time robot planning using Behavior Tree." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2021 (2021): 1A1—E18. http://dx.doi.org/10.1299/jsmermd.2021.1a1-e18.

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35

Hasegawa, Tsutomu. "Special Issue on Planning and Intelligent Behavior." Journal of Robotics and Mechatronics 4, no. 5 (October 20, 1992): 363. http://dx.doi.org/10.20965/jrm.1992.p0363.

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A required function of intelligent robots is autonomous and quick execution of tasks which are difficult for conventional machines. In addition, the intention of human operators must be transmitted precisely and easily to the robots. A variety of R&D is underway in order to realize such requirements. This R&D falls into two categories: (1) R&D on intelligent functions applied for the preparation phase of task execution and (2) that applied for the real time task execution. Motion planning based on geometrical information is a typical function for the task preparation phase which has been studied for the past ten years. Thanks to the rapid progress in computing power, the analysis of real problems has progressed and has permitted the practical application of such planning. Thus, its application to operational use is not far off. R&D on a comprehensive system including the geometric environment modeling, motion planning, and real time task execution is also underway. Intelligent functions necessary for task execution must include a task execution mechanism and a control method which guarantee reliable task execution in the presence of unpredictable errors. The solution to this problem will be realized through the implementation of skillful manipulator motions which utilize various sensors and constraints being complied in the real world, most as key technologies. This special issue has compiled reviews and articles which focus on the above mentioned issues.

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36

HAMATANI, Naoto. "THE DEVELOPMENT OF PLANNING IN CHILDREN'S BEHAVIOR." Japanese Journal of Educational Psychology 35, no. 4 (1987): 326–34. http://dx.doi.org/10.5926/jjep1953.35.4_326.

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GREENE, JOHN O., H. O'HAIR, MICHAEL J. CODY, and CATHERINE YEN. "PLANNING AND CONTROL OF BEHAVIOR DURING DECEPTION." Human Communication Research 11, no. 3 (March 1985): 335–64. http://dx.doi.org/10.1111/j.1468-2958.1985.tb00051.x.

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38

Dehaene, S., and J. P. Changeux. "A hierarchical neuronal network for planning behavior." Proceedings of the National Academy of Sciences 94, no. 24 (November 25, 1997): 13293–98. http://dx.doi.org/10.1073/pnas.94.24.13293.

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Oetting, E. R. "Planning Programs for Prevention of Deviant Behavior:." Drugs & Society 6, no. 3-4 (July 1992): 313–44. http://dx.doi.org/10.1300/j023v06n03_06.

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40

Conner, Mark, Tracy Sandberg, and Paul Norman. "Using Action Planning to Promote Exercise Behavior." Annals of Behavioral Medicine 40, no. 1 (May 6, 2010): 65–76. http://dx.doi.org/10.1007/s12160-010-9190-8.

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41

Liu, Dong, Ming Cong, Yu Du, Qiang Zou, and Yingxue Cui. "Robotic autonomous behavior selection using episodic memory and attention system." Industrial Robot: An International Journal 44, no. 3 (May 15, 2017): 353–62. http://dx.doi.org/10.1108/ir-09-2016-0250.

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Purpose This paper aims to focus on the autonomous behavior selection issue of robotics from the perspective of episodic memory in cognitive neuroscience with biology-inspired attention system. It instructs a robot to follow a sequence of behaviors. This is similar to human travel to a target location by guidance. Design/methodology/approach The episodic memory-driving Markov decision process is proposed to simulate the organization of episodic memory by introducing neuron stimulation mechanism. Based on the learned episodic memory, the robotic global planning method is proposed for efficient behaviors sequence prediction using bottom-up attention. Local behavior planning based on risk function and feasible paths is used for behavior reasoning under imperfect memory. Aiming at the problem of whole target selection under redundant environmental information, a top-down attention servo control method is proposed to effectively detect the target containing multi-parts and distractors which share same features with the target. Findings Based on the proposed method, the robot is able to accumulate experience through memory, and achieve adaptive behavior planning, prediction and reasoning between tasks, environment and threats. Experimental results show that the method can balance the task objectives, select the suitable behavior according to current environment. Originality/value The behavior selection method is integrated with cognitive levels to generate optimal behavioral sequence. The challenges in robotic planning under uncertainty and the issue of target selection under redundant environment are addressed.

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42

Yue-wen, Fu, Li Meng, Liang Jia-hong, and Hu Xiao-qian. "Optimal Acceleration-Velocity-Bounded Trajectory Planning in Dynamic Crowd Simulation." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/501689.

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Creating complex and realistic crowd behaviors, such as pedestrian navigation behavior with dynamic obstacles, is a difficult and time consuming task. In this paper, we study one special type of crowd which is composed of urgent individuals, normal individuals, and normal groups. We use three steps to construct the crowd simulation in dynamic environment. The first one is that the urgent individuals move forward along a given path around dynamic obstacles and other crowd members. An optimal acceleration-velocity-bounded trajectory planning method is utilized to model their behaviors, which ensures that the durations of the generated trajectories are minimal and the urgent individuals are collision-free with dynamic obstacles (e.g., dynamic vehicles). In the second step, a pushing model is adopted to simulate the interactions between urgent members and normal ones, which ensures that the computational cost of the optimal trajectory planning is acceptable. The third step is obligated to imitate the interactions among normal members using collision avoidance behavior and flocking behavior. Various simulation results demonstrate that these three steps give realistic crowd phenomenon just like the real world.

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43

Hurtado, María, and Gabriela Topa. "Health Behavior and Health and Psychosocial Planning for Retirement among Spanish Health Professionals." Journal of Clinical Medicine 7, no. 12 (November 28, 2018): 495. http://dx.doi.org/10.3390/jcm7120495.

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The aging of the workforce among health professionals demands attention to the study of their health behavior before retirement. The aim of the present study is to analyze the relationships between health planning and psychosocial planning—on the one hand—and health professionals’ health behavior, on the other. In addition, we will study the mediator role of public protection, self-insurance, and self-protection in the relationship between planning and health behavior. The sample includes 169 healthcare professionals from a public hospital in Spain. A serial mediation model estimating all of the parameters simultaneously was tested. The findings have confirmed the relationship between health planning and health behavior, as well as the serial mediation of the behaviors in this relationship. As the main causes of death in the Western world are not transmittable diseases, but cardiovascular diseases, diabetes, and other diseases that are closely linked to lifestyle factors, our findings strongly support that we are responsible for our long-term health status and well-being.

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44

Reece, Douglas A. "Movement Behavior for Soldier Agents on a Virtual Battlefield." Presence: Teleoperators and Virtual Environments 12, no. 4 (August 2003): 387–410. http://dx.doi.org/10.1162/105474603322391622.

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We have developed a movement behavior model for soldier agents who populate a virtual battlefield environment. Whereas many simulations have addressed human movement behavior before, none of them has comprehensively addressed realistic military movement at individual and unit levels. To design an appropriate movement behavior model, we found it necessary to elaborate all of the requirements on movement from the military tasks of interest, define a behavior architecture that encompasses all required movement tasks, select appropriate movement planning and control approaches in light of the requirements, and implement the planning and control algorithms with novel enhancements to achieve satisfactory results. The breadth of requirements in this problem domain makes simple behavior architectures inadequate and prevents any single planning approach from easily accomplishing all tasks. In our behavior architecture, a hierarchy of tasks is distributed over unit leaders and unit members. For movement planning, we use an A* search algorithm on a hybrid search space comprising a two-dimensional regular grid and a topological map; the plan produced is a series of waypoints annotated with posture and speed changes. Individuals control movement with reactive steering behaviors. The result is a system that can realistically plan and execute a variety of unit and individual agent movement tasks on a virtual battlefield.

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Dortmans, Eric, and Teade Punter. "Behavior Trees for Smart Robots Practical Guidelines for Robot Software Development." Journal of Robotics 2022 (September 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/3314084.

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Behavior Trees are a promising approach to model the autonomous behaviour of robots in dynamic environments. Behavior Trees represent action selection decisions as a tree of decision nodes. The hierarchy of these decision nodes provides the planning of actions of the robot including its reactions on exceptions. Behavior Trees enable flexible planning and replanning of robot behavior while supporting better maintainable decision-making than traditional Finite State Machines. This paper presents an overview of lessons, which we have learned when applying Behavior Trees to various autonomous robots. We present these lessons as a sequence of steps that is meant to support robot software practitioners to develop their systems.

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Pakpour, Amir H., Cheng-Kuan Lin, Mahdi Safdari, Chung-Ying Lin, Shun-Hua Chen, and Kyra Hamilton. "Using an Integrated Social Cognition Model to Explain Green Purchasing Behavior among Adolescents." International Journal of Environmental Research and Public Health 18, no. 23 (December 1, 2021): 12663. http://dx.doi.org/10.3390/ijerph182312663.

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Strengthening pro-environmental behaviors such as green purchasing behavior is important for environmental sustainability. An integrated social cognition model which incorporates constructs from habit theory, health action process approach (HAPA), and theory of planned behavior (TPB) is adopted to understand Iranian adolescents’ green purchasing behavior. Using a correlational-prospective design, the study recruited Iranian adolescents aged between 14 and 19 years (N = 2374, n = 1362 (57.4%) females, n = 1012 (42.6%) males; Mean (SD) age = 15.56 (1.22)). At baseline (T1), participants self-reported on the following constructs: past behavior; habit strength (from habit theory); action planning and coping planning (from HAPA); and intention, perceived behavioral control, subjective norm, and attitude (from TPB) with respect to green purchasing behavior. Six months later (T2), participants self-reported on their actions in terms of purchasing green goods. Our findings reported direct effects of perceived behavioral control, subjective norms, attitude, and past behavior on intention; intention and perceived behavioral control on green purchase behavior; intention on two types of planning (i.e., action and coping planning); both types of planning on green purchase behavior; and past green purchase behavior and habits on prospectively measured green purchase behavior. These results indicate that adolescent green purchasing behavior is underpinned by constructs representing motivational, volitional, and automatic processes. This knowledge can help inform the development of theory-based behavior change interventions to improve green purchasing in adolescents, a key developmental period where climate change issues are salient and increased independence and demands in making self-guided decisions are needed.

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Pujinda, Panit, and Sauvanithi Yupho. "The Paradoxical Travel Behavior of Bangkokians." Environment-Behaviour Proceedings Journal 2, no. 5 (March 19, 2017): 393. http://dx.doi.org/10.21834/e-bpj.v2i5.706.

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Bangkok has been ranked as the world’s most traffic jam more than decades. At the same time, the city is constantly developed with many maga projects with the attempt to heal the problem. However, the developments in Bangkok do not follow transportation planning as elaborated in this paper through three basic expectations of travel behavior. They based on transportation planning in Bangkok are: (1) heavy rail transit that runs on a radial line will transport passengers from residential neighborhoods in outer Bangkok to the central business district (CBD); (2) If the workplace is fixed, persons who live in outer Bangkok are assumed to have higher travel cost and commuting time than those who live in the city center; and (3). A feeder system will support heavy rail transit by expanding service areas and increasing passengers. However, this paper documents how Thai travel behavior is not necessarily conforming to expectations.Keywords: Quality of Life; Travel Behavior; Public Transportation; BangkokISSN: 2398-4287© 2017. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.

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Burkert, Silke, Urte Scholz, Oliver Gralla, Jan Roigas, and Nina Knoll. "Dyadic planning of health-behavior change after prostatectomy: A randomized-controlled planning intervention." Social Science & Medicine 73, no. 5 (September 2011): 783–92. http://dx.doi.org/10.1016/j.socscimed.2011.06.016.

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AA, Gareev. "Environmental Issues of Transient Behavior." Petroleum & Petrochemical Engineering Journal 5, no. 4 (2021): 1–10. http://dx.doi.org/10.23880/ppej-16000284.

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We are currently witnessing a high-rate of exploitation of oil fields at the stage of declining production. This decline is a result of the depletion of oil fields, which leads to the use of electric submersible centrifugal pumps (ESP) with dynamic head below saturation pressure. In this operating mode, the ESP’s efficiency deteriorates, leading to the overheating of its working elements during operation. The boiling of water contained in the wellstream and salt deposition can take place in an overheated pump. This, in turn, can lead to a premature failure of the centrifugal pump and reduce the economic viability of this oil production method. To eliminate premature failure, such units must be transferred to an operating mode with periodic shutdowns. Yet, the planning of trip-out and restart time schedules is performed in the absence of a proper theoretical justification. Such planning often leads to ESP failures due to the reduction of the electric resistance of cable lines or salt deposition. To prevent salt deposition, oil production companies use different chemicals, which are pumped into the hole annulus and are expected to stop salt deposition when propelled into the pump pot. Chemical treatment practice shows that these reagents perform poorly and may not prevent salt deposition at all. In reality, chemical compounds can damage downhole equipment and its structural elements. Long-term use of these reagents may lead to ecological disaster – the contamination of productive formations and confined groundwater beds. This work investigates the possibility of ESP operation in periodic mode without salt deposition and the exploitation of oil fields without the application of chemical reagents. The development of a periodic operation method allowing for ESP operation without salt deposition could eliminate the use of reagents in oil production and thereby reduce the risk of ecological disasters.

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Panov, A. I. "Goal Setting and Behavior Planning for Cognitive Agents." Scientific and Technical Information Processing 46, no. 6 (December 2019): 404–15. http://dx.doi.org/10.3103/s0147688219060066.

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