Francisco Grimaldo

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.

Mechanism change in a simulation of peer review: from junk support to elitism

Peer review works as the hinge of the scientific process, mediating between research and the awareness/acceptance of its results. While it might seem obvious that science would regulate itself scientifically, the consensus on peer review is eroding; a deeper understanding of its workings and potential alternatives is sorely needed. Employing a theoretical approach supported by agent-based simulation, we examined computational models of peer review, performing what we propose to call redesign, that is, the replication of simulations using different mechanisms. Here, we show that we are able to obtain the high sensitivity to rational cheating that is present in literature. In addition, we also show how this result appears to be fragile against small variations in mechanisms. Therefore, we argue that exploration of the parameter space is not enough if we want to support theoretical statements with simulation, and that exploration at the level of mechanisms is needed. These findings also support prudence in the application of simulation results based on single mechanisms, and endorse the use of complex agent platforms that encourage experimentation of diverse mechanisms.

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.

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).

Distributed Cognition Learning in Collaborative Civil Engineering Projects Management

Due to the diversity and complexity of its projects, the Civil Engineering domain has historically encompassed very heterogeneous disciplines. From the beginning, any Civil Infrastructure project is systematically divided into smaller subprojects in order to reduce or isolate the overall complexity. However, as a parallel design work, these subdesigns may experience divergences which often lead to design conflicts when they are merged back to the global design. If a high-quality design is desired, these conflicts need to be detected and solved.We present a Multiagent system able to manage these design conflicts by detecting them, by assisting the engineers in the negotiation of solutions, and finally by learning how to solve future similar problems. The advantage of the system is that what is learned is not one individual’s knowledge but the project’s global distributed cognition.

An Agents and Artifacts Approach to Distributed Data Mining

This paper proposes a novel Distributed Data Mining (DDM) approach based on the Agents and Artifacts paradigm, as implemented in CArtAgO [9], where artifacts encapsulate data mining tools, inherited from Weka, that agents can use while engaged in collaborative, distributed learning processes. Target hypothesis are currently constrained to decision trees built with J48, but the approach is flexible enough to allow different kinds of learning models. The twofold contribution of this work includes: i) JaCA-DDM: an extensible tool implemented in the agent oriented programming language Jason [2] and CArtAgO [10,9] to experiment DDM agent-based approaches on different, well known training sets. And ii) A collaborative protocol where an agent builds an initial decision tree, and then enhances this initial hypothesis using instances from other agents that are not covered yet (counter examples); reducing in this way the number of instances communicated, while preserving accuracy when compared to full centralized approaches.

Integrating social skills in task-oriented 3D IVA

This paper presents a set of mechanisms oriented to incorporate social information into the decision taking of task-oriented 3DIVA. The aim of this approach is to integrate collaborative skills in different characters roles (seller/buyer, worker, pedestrian, etc.) in order to enhance its behavioral animation. The collective intelligence expected in this kind of multi-character domains (e.g. storytelling, urban simulation, interactive games, etc.) requires agents able to dialogue/interact with other characters, to autonomously group/ungroup (according to their goals), or to distribute tasks and coordinate their execution for solving possible conflicts. The social model implemented follows the definitions for collaborative agents, since agents use communicative acts to cooperate. In this context, collaboration derives mainly from two points: team formation (grouping for 3DIVA) and task coordination (reducing dependences between agent activities). Finally, we show the results obtained in 3D multi-character simulations (resource competition), created to verify the social behavior introduced.

Assessing peer review by gauging the fate of rejected manuscripts: the case of the Journal of Artificial Societies and Social Simulation

This paper investigates the fate of manuscripts that were rejected from JASSS-The Journal of Artificial Societies and Social Simulation, the flagship journal of social simulation. We tracked 456 manuscripts that were rejected from 1997 to 2011 and traced their subsequent publication as journal articles, conference papers or working papers. We compared the impact factor of the publishing journal and the citations of those manuscripts that were eventually published against the yearly impact factor of JASSS and the number of citations achieved by the JASSS mean and top cited articles. Only 10% of the rejected manuscripts were eventually published in a journal that was indexed in the Web of Science, although most of the rejected manuscripts were published elsewhere. Being exposed to more than one round of reviews before rejection, having received a more detailed reviewer report and being subjected to higher inter-reviewer disagreement were all associated with the number of citations received when the manuscript was eventually published. This indicates that peer review could contribute to increasing the quality even of rejected manuscripts.