Catálogo de publicaciones - libros

Mobile agent technology has been promoted as an emerging technology that makes it much easier to design, implement, and maintain distributed systems. It also provides an infrastructure for multi-agent computing. This chapter discusses the potential uses of mobile agents in distributed systems, lists their potential advantages and disadvantages. The body of the chapter has descriptions of technologies for executing, migrating, and implementing mobile agents. It also presents several actual and potential applications of mobile agents. A brief review of other research in the area and prospects for the future conclude the chapter.

There has been a significant increase in the use of multi-agent social-network models due to their ability to flexibly model emergent behaviors in complex socio-technical Systems while linking to real data. These models are growing in size and complexity which requires significant time and effort to calibrate, validate, improve the model, and gain insight into model behavior. In this paper, we present our knowledge-based simulation-aided approach for automating model-improvement and our tool implementing this approach (WIZER). WIZER is capable of calibrating and validating multi-agent social-network models, and facilitates model-improvement and understanding. By employing knowledge-based search, causal analysis, and simulation control and inference techniques, WIZER can reduce the number of simulation runs needed to calibrate, validate, and improve a model and improve the focus of these runs. WIZER automates reasoning and analysis of simulations, instead of being a multi-agent programming language or environment. We ran a preliminary version of WIZER on BioWar a city-scale social agent network Simulation of the effects of weaponized biological attacks on a demographically-realistic population within a background of naturally-occurring diseases. The results demonstrate the efficacy of WIZER.

Agents monitor other agents to coordinate and collaborate robustly. The goals of such monitoring include detection of coordination failures. However, as the number of monitored agents is scaled up, two key challenges arise: (i) Agents become physically and logically unconnected (unobservable) to their peers; and (ii) the number of possible coordination failures grows exponentially, with all potential interactions. This paper examines these challenges in teams of cooperating agents. We provide a brief survey of the evolution of two key approaches to handling coordination failures in large-scale teams: Restricting the number of agents that must be monitored, and using model-based rather than fault-based detection methods. We focus on a monitoring task that is of particular importance to robust teamwork: detecting disagreements among team-members.

As a paradigm for coordinating cooperative agents in dynamic environments, teamwork has been shown to be capable of leading to flexible and robust behavior. However, when teamwork is applied to the problem of building teams with hundreds of members, its previously existing, fundamental limitations become apparent. In this paper, we address the limitations of existing models as they apply to very large agent teams. We develop algorithms aimed at flexible and efficient coordination, applying a decentralized social network topology for team organization and the abstract coordination behaviors of Team Oriented Plans (TOPs). From this basis, we present a model to organize a team into dynamically evolving subteams, in order to flexibly coordinate the team. Additionally, we put forward a novel approach to sharing information within large teams, which provides for targeted, efficient information delivery with a localized reasoning process model built on previously incoming information. We have developed domain- independent software proxies, with which we demonstrate teams of an order of magnitude larger than those previously discussed in known published work. We implement the results of our approach, demonstrating its ability to handle the challenges of coordinating large agent teams.