Fernando Barber

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.

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).

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.

An Efficient Synthetic Vision System for 3D Multi-character Systems

This poster deals with the problem of sensing virtual environments for 3D intelligent multi-character simulations. As these creatures should display reactive skills (navigation or gazing), together with the necessary planning processes, required to animate their behaviours, we present an efficient and fully scalable sensor system designed to provide this information (low level + high level) to different kinds of 3D embodied agents (games, storytelling, etc).

Integrating minimin-hsp agents in a dynamic simulation framework

In this paper, we describe the framework created for implementing AI-based animations for artificial actors in the context of IVE (Intelligent Virtual Environments). The minMin-HSP (Heuristic Search Planner) planner presented in [12] has been updated to deal with 3D dynamic simulation environments, using the sensory/actuator system fully implemented in UnrealTM and presented in [10]. Here, we show how we have integrated these systems to handle the necessary balance between the reactive and deliberative skills for 3D Intelligent Virtual Agents (3DIVAs). We have carried out experiments in a multi-agent 3D blocks world, where 3DIVAs will have to interleave sensing, planning and execution to be able to adapt to the enviromental changes without forgetting their goals. Finally, we discuss how the HSP agents created are adequated to animate the intelligent behaviour of 3D simulation actors.

A Multiagent Framework to Animate Socially Intelligent Agents

This paper presents a multiagent framework designed to animate groups of synthetic humans that properly balance task oriented and social behaviors. The work presented in this paper focuses on the BDI agents and the social model integrated to provide socially acceptable decisions. The social model provides rationality, to control the global coordination of the group, and sociability, to simulate relations (e.g. friends) and reciprocity between members. The multiagent based framework has been tested successfully in dynamic environments while simulating a virtual university bar, where several types of agents (groups of waiters and customers) can interact and finally display complex social behaviors (e.g. task passing, reciprocity, planned meetings).

Discovering Temporal Relationships in Databases of Newspapers

This paper is mainly dedicated to analyse the problem of discovering frequent temporal patterns in event sequences extracted from a large repository of newspapers. The proposed formalism and algorithms rely on Toodor, which is a document retrieval system that allows users to specify conditions over the structure, contents and temporal features of the stored documents. We develop in this work several algorithms for recognising frequent temporal patterns in terms of arc-consistency, which consist of discarding temporal occurrences that do not satisfy a temporal structure.

Multiagent System for Detecting and Solving Design-Time Conflicts in Civil Infrastructure

One typical source of problems in the Civil Infrastructure domain is the distributed and collaborative nature of the projects in which different profiles of engineers contribute with designs devoted to the interest of their field of expertise. Thus, situations in which there are different conflicts of interests are quite common. A conflict refers to a situation in which the actions of an engineer collide with the interests of other engineers. In this paper, we present a multi-agent system that, thanks to the use of ontologies and rules on those ontologies, is able to detect profilespecific conflict situations and solve them according to the preferences of the parties involved in the conflict. The conflict solving is based on the Multi Agent Resource Allocation (MARA) theory. The system is applied to a real use case of an urban development where both the road network and the buildings are designed.

Towards a model for urban mobility social simulation

Metropolitan mobility models, mainly based on the massive use of the car instead of the public transportation, will soon become unsustainable unless there is a change of citizens’ minds and transport policies. The main challenge related to urban mobility is that of getting free-flowing greener cities, which are provided with a smarter and accessible urban transport system. In this paper, we present an agent-based social simulation approach to tackle this kind of socio-ecological systems. The Jason Multi-modal Agent Decision Making (J-MADeM) library enables us to model and implement the social decisions made by each habitant about how to get to work every day, e.g., by train, by car, sharing a car, etc. In this way, we focus on the decision-making aspects of this problem at a micro-level, instead of focussing on spatial or other macro-issues. The first results show the different outcomes produced by societies of individualist and egalitarian agents, in terms of the average travel time, the use of the urban transportation and the amount of CO2 emitted to the environment.