3,993 research outputs found
Overview of crowd simulation in computer graphics
High-powered technology use computer graphics in education, entertainment, games, simulation, and virtual heritage applications has led it to become an important area of research. In simulation, according to Tecchia et al. (2002), it is important to create an interactive, complex, and realistic virtual world so that the user can have an immersive experience during navigation through the world. As the size and complexity of the environments in the virtual world increased, it becomes more necessary to populate them with peoples, and this is the reason why rendering the crowd in real-time is very crucial. Generally, crowd simulation consists of three important areas. They are realism of behavioral (Thompson and Marchant 1995), high-quality visualization (Dobbyn et al. 2005) and convergence of both areas. Realism of behavioral is mainly used for simple 2D visualizations because most of the attentions are concentrated on simulating the behaviors of the group. High quality visualization is regularly used for movie productions and computer games. It gives intention on producing more convincing visual rather than realism of behaviors. The convergences of both areas are mainly used for application like training systems. In order to make the training system more effective, the element of valid replication of the behaviors and high-quality visualization is added
An information theory based behavioral model for agent-based crowd simulations
Crowds must be simulated believable in terms of their appearance and behavior to improve a virtual environment’s realism. Due to the complex nature of human behavior, realistic behavior of agents in crowd simulations is still a challenging problem. In this paper, we propose a novel behavioral model which builds analytical maps to control agents’ behavior adaptively with agent-crowd interaction formulations. We introduce information theoretical concepts to construct analytical maps automatically. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses
of the presented behavior model with measured crowd data and two agent-based crowd simulators
Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems
Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which
explores the tremendous data collected by mobile smart devices with prominent
spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing
Systems, temporally recruited mobile users can provide agile, fine-grained, and
economical sensing labors, however their self-interest cannot guarantee the
quality of the sensing data, even when there is a fair return. Therefore, a
mechanism is required for the system server to recruit well-behaving users for
credible sensing, and to stimulate and reward more contributive users based on
sensing truth discovery to further increase credible reporting. In this paper,
we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile
Crowdsensing Systems, which achieves credibility-driven user recruitment and
payback maximization for honest users with quality data. Via theoretical
analysis, we demonstrate the correctness of our design. The performance of our
scheme is evaluated based on extensive realworld trace-driven simulations. Our
evaluation results show that our scheme is proven to be effective in terms of
both guaranteeing sensing accuracy and resisting potential cheating behaviors,
as demonstrated in practical scenarios, as well as those that are intentionally
harsher
PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data
Emergence of smartphone and the participatory sensing (PS) paradigm have
paved the way for a new variant of pervasive computing. In PS, human user
performs sensing tasks and generates notifications, typically in lieu of
incentives. These notifications are real-time, large-volume, and multi-modal,
which are eventually fused by the PS platform to generate a summary. One major
limitation with PS is the sparsity of notifications owing to lack of active
participation, thus inhibiting large scale real-life experiments for the
research community. On the flip side, research community always needs ground
truth to validate the efficacy of the proposed models and algorithms. Most of
the PS applications involve human mobility and report generation following
sensing of any event of interest in the adjacent environment. This work is an
attempt to study and empirically model human participation behavior and event
occurrence distributions through development of a location-sensitive data
simulation framework, called PS-Sim. From extensive experiments it has been
observed that the synthetic data generated by PS-Sim replicates real
participation and event occurrence behaviors in PS applications, which may be
considered for validation purpose in absence of the groundtruth. As a
proof-of-concept, we have used real-life dataset from a vehicular traffic
management application to train the models in PS-Sim and cross-validated the
simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International
Conference on Smart Computing (SMARTCOMP-2018
Improving games AI performance using grouped hierarchical level of detail
Computer games are increasingly making use of large environments; however, these are often only sparsely populated with autonomous agents. This is, in part, due to the computational cost of implementing behaviour functions for large numbers of agents.
In this paper we present an optimisation based on level of detail which reduces the overhead of modelling group behaviours, and facilitates the population of an expansive game world.
We consider an environment which is inhabited by many distinct groups of agents. Each group itself comprises individual agents, which are organised using a hierarchical tree structure. Expanding and collapsing nodes within each tree allows the efficient dynamic abstraction of individuals, depending on their proximity to the player. Each branching level represents a different level of detail, and the system is designed to trade off computational performance against behavioural fidelity in a way which is both efficient and seamless to the player.
We have developed an implementation of this technique, and used it to evaluate the associated performance benefits. Our experiments indicate a significant potential reduction in processing time, with the update for the entire AI system taking less than 1% of the time required for the same number of agents without optimisation
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