Trusting Groups in Coalition Formation using Social Distance

In environments where distributed team formation is key, and defections are possible, the use of trust as social capital allows social norms to be defied and compared. An agent can use this information, when invited to join a group or collation, to decide whether or not its utility will be increased by joining. In this work a social network approach is used to define and reason about the relationships contained in the agent community. Previous baseline work is extended with two decision making mechanisms. These are compared by simulating an abstract grid-like environment, and preliminary results are reported.

Laugh-aware virtual agent and its impact on user amusement

In this paper we present a complete interactive system en- abled to detect human laughs and respond appropriately, by integrating the information of the human behavior and the context. Furthermore, the impact of our autonomous laughter-aware agent on the humor experience of the user and interaction between user and agent is evaluated by sub- jective and objective means. Preliminary results show that the laughter-aware agent increases the humor experience (i.e., felt amusement of the user and the funniness rating of the film clip), and creates the notion of a shared social experience, indicating that the agent is useful to elicit posi- tive humor-related affect and emotional contagion.

Middleware mechanisms for agent mobility in wireless sensor and actuator networks

This paper describes middleware-level support for agent mobility, targeted at hierarchically structured wireless sensor and actuator network applications. Agent mobility enables a dynamic deployment and adaptation of the application on top of the wireless network at runtime, while allowing the middleware to optimize the placement of agents, e.g., to reduce wireless network traffic, transparently to the application programmer. The paper presents the design of the mechanisms and protocols employed to instantiate agents on nodes and to move agents between nodes. It also gives an evaluation of a middleware prototype running on Imote2 nodes that communicate over ZigBee. The results show that our implementation is reasonably efficient and fast enough to support the envisioned functionality on top of a commodity multi-hop wireless technology. Our work is to a large extent platform-neutral, thus it can inform the design of other systems that adopt a hierarchical structuring of mobile components. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

The Belfast Storytelling Database: A spontaneous social interaction database with laughter focused annotation

To support the endeavor of creating intelligent interfaces between computers and humans the use of training materials based on realistic human-human interactions has been recognized as a crucial task. One of the effects of the creation of these databases is an increased realization of the importance of often overlooked social signals and behaviours in organizing and orchestrating our interactions. Laughter is one of these key social signals; its importance in maintaining the smooth flow of human interaction has only recently become apparent in the embodied conversational agent domain. In turn, these realizations require training data that focus on these key social signals. This paper presents a database that is well annotated and theoretically constructed with respect to understanding laughter as it is used within human social interaction. Its construction, motivation, annotation and availability are presented in detail in this paper.

Uses of Agent-Based Modeling for Health Communication: the TELL ME Case Study

Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals' protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.