Miguel Lozano

A comparative study of partitioning methods for crowd simulations

The simulation of large crowds of autonomous agents with realistic behavior is still a challenge for several computer research communities. In order to handle large crowds, some scalable architectures have been proposed. Nevertheless, the effective use of distributed systems requires the use of partitioning methods that can properly distribute the workload generated by agents among the existing distributed resources. In this paper, we analyze the use of irregular shape regions (convex hulls) for solving the partitioning problem. We have compared a partitioning method based on convex hulls with two techniques that use rectangular regions. The performance evaluation results show that the convex hull method outperforms the rest of the considered methods in terms of both fitness function values and execution times, regardless of the movement pattern followed by the agents. These results show that the shape of the regions in the partition can improve the performance of the partitioning method, rather than the heuristic method used.

Simulating socially intelligent agents in semantic virtual environments

The simulation of synthetic humans inhabiting virtual environments is a current research topic with a great number of behavioral problems to be tackled. Semantical virtual environments (SVEs) have recently been proposed not only to ease world modeling but also to enhance the agent–object and agent–agent interaction. Thus, we propose the use of ontologies to define the world’s knowledge base and to introduce semantic levels of detail that help the sensorization of complex scenes—containing lots of interactive objects. The object taxonomy also helps to create general and reusable operativity for autonomous characters—for example, liquids can be poured from containers such as bottles. On the other hand, we use the ontology to define social relations among agents within an artificial society. These relations must be taken into account in order to display socially acceptable decisions. Therefore, we have implemented a market-based social model that reaches coordination and sociability by means of task exchanges. This paper presents a multi-agent framework oriented to simulate socially intelligent characters in SVEs. The framework has been successfully tested in three-dimensional (3D) dynamic scenarios while simulating a virtual university bar, where groups of waiters and customers interact with both the objects in the scene and the other virtual agents, finally displaying complex social behaviors.

A new system architecture for crowd simulation

Crowd simulation requires both rendering visually plausible images and managing the behavior of autonomous agents. Therefore, these applications need an efficient design that allows them to simultaneously handle these two requirements. Although several proposals have focused on the software architectures for these systems, no proposals have focused on the computer systems supporting them. In this paper, we analyze the computer architectures used in the literature to support distributed virtual environments. Also, we propose a distributed computer architecture which is efficient enough to support simulations of thousand of autonomous agents. This proposal consists of a cluster of interconnected computers in order to improve flexibility and robustness, as well as a hierarchical software architecture that efficiently provides consistency. Performance evaluation results show that the trade-off between flexibility and consistency allows to efficiently manage thousands of autonomous agents. Therefore, this network-based system architecture can provide the required scalability for large-scale crowd simulations.

Workload balancing in distributed crowd simulations: the partitioning method

The simulation of large crowds of autonomous agents with a realistic behavior is still a challenge for several computer research communities. Distributed architectures can provide scalability to crowd simulations, but they require the use of efficient partitioning methods. Although convex hulls have been shown as very efficient structures for crowd partitioning, providing efficient workload balancing to large scale simulations is still an open issue. In this paper, we propose the integration of a workload balancing technique for crowd simulations within a partitioning method based on convex hulls. The region-based balancing technique reassigns agents to servers using a criterion of distance. The performance evaluation results show that this technique ensures the saturation avoidance of the servers in an homogeneous distributed system. This feature can increase the scalability of crowd simulations.

Towards a Simulator of Integrated Long-term Care Systems for Elderly People

In this paper, we propose a simulator for integrated long-term care systems using as a starting point a holistic model of care systems for people that need long term care, the Sustainable Socio-Health Model (SSHM). The implementation of the simulator on the Jason multi-agent platform allows the tool to include the human interactions, preferences, and social abilities that take place between elderly people and the staff of healthcare systems (doctors, social workers and nurses). In addition, the use of this multi-agent platform provides the required scalability for simulating population sizes of different orders of magnitude. The paper shows the model to be implemented in the simulator, the simulator architecture, the types of agents considered, their functionality and the information flow among them. Additionally, it shows the validation of the simulator with real data obtained from empirical studies conducted by the Polibienestar Research Institute in Spain, as well as a performance evaluation that sketches the performance of the simulator when using the centralized Jason infrastructure under different population sizes. Effectively, simulation can provide policy makers with the option of going into a decision theatre and virtually knowing the consequences of different policies prior to determining the real policy to be adopted.

A GPU-Based Multi-agent System for Real-Time Simulations

The huge number of cores existing in current Graphics Processor Units (GPUs) provides these devices with computing capabilities that can be exploited by distributed applications. In particular, these capabilites have been used in crowd simulations for enhancing the crowd rendering, and even for simulating continuum crowds. However, GPUs have not been used for simulating large crowds of complex agents, since these simulations require distributed architectures that can support huge amounts of agents. In this paper, we propose a GPU-based multi-agent system for crowd simulation. Concretely, we propose the use of an on-board GPU to implement one of the main tasks that a distributed server for crowd simulations should perform. The huge number of cores in the GPU is used to simultaneously validate movement requests from different agents, greatly reducing the server response time. Since this task represents the critical data path, the use of this hardware significantly increases the parallelism achieved with respect to the implementation of the same distributed server on a CPU. An application example shows that the system can support agents with complex navigational behaviors.

Animating groups of Socially Intelligent Agents

This paper presents a multi-agent framework oriented to animate groups of synthetic humans that properly balance task-oriented and social behaviors. We mainly focus on the social model designed for BDI-agents to display socially acceptable decisions. This model is based on an auction mechanism used to coordinate the group activities derived from the characters roles. The model also introduces reciprocity relations between the members of a group and allows the agents to include social tasks to produce realistic behavioral animations. Furthermore, a conversational library provides the set of plans to manage social interactions and to animate from simple chats to more complex negotiations. The framework has been successfully tested in a 3D dynamic environment while simulation a virtual university bar, where groups of waiters and customers can interact and finally display complex social behaviors (e.g. task passing, reciprocity, planned meetings...).

Coordination and sociability for intelligent virtual agents

This paper presents a multi-agent framework designed to simulate synthetic humans that properly balance task oriented and social behaviors. The work presented in this paper focuses on the social library integrated in BDI agents to provide socially acceptable decisions. We propose the use of ontologies to define the social relations within an artificial society and the use of a market based mechanism to reach sociability by means of task exchanges. The social model balances rationality, to control the global coordination of the group, and sociability, to simulate relations (e.g. friendliness) and reciprocity among agents. The multi-agent framework has been tested successfully in dynamic environments while simulating a virtual bar, where groups of waiters and customers can interact and finally display complex social behaviors (e.g. task passing, reciprocity, planned meetings).

A distributed visualization system for crowd simulations

The visualization system of large-scale crowd simulations should scale up with both the number of visuals views of the virtual world and the number of agents displayed in each visual. Otherwise, we could have large scale crowd simulations where only a small percentage of the population is displayed. Several approaches have been proposed in order to efficiently render crowds of animated characters. However, these approaches either render crowds animated with simple behaviors or they can only support a few hundreds of user-driven entities. In this paper, we propose a distributed visualization system for large crowds of autonomous agents that allows the visualization of crowds animated with complex behaviors without adding significant overhead to the simulation servers. The proposed implementation can be hosted on dedicated computers different from the servers, and it takes advantage of the Graphics Processor Unit GPU capabilities. As a result, the performance evaluation shows that thousands of agents can be rendered without affecting the system performance. Also, the results show that the design of the visual client allows to add multiple visuals for displaying large crowds.

A scalable multiagent system architecture for interactive applications

Interactive applications like crowd simulations need to properly render the virtual world while simulating the interaction of thousands of agents at the same time. The computational workload generated by these two tasks highly increases with the number of the simulated agents, requiring a scalable design of the multiagent system. In this paper, we present, in an unified manner, a distributed multiagent system architecture that can manage large crowds of autonomous agents at interactive rates while rendering multiple views of the virtual world being simulated. This architecture consists of a distributed multiagent system and a complementary distributed visualization subsystem. We also present a new scalability study of the distributed multiagent system architecture, as well as an application example. The scalability study shows that, since no system bottlenecks are present in the proposed multiagent system architecture, it can efficiently simulate population sizes of different orders of magnitude by simply adding more hardware, provided that the ratio of clients per server is appropriate for the computer platforms acting as servers. On the other hand, the application example shows that these improvements are achieved without affecting the interactivity of the simulation. Therefore, the scalability of the proposed multiagent system architecture is validated for interactive large-scale applications.