Resumo em Inglês:

Law enforcement approaches have been proposed to promote dependability in open multi-agent systems. Interaction laws are defined and then enforced to promote predictability. As new software demands and requirements appear, the system and its interaction laws must evolve to support those changes. The purpose of domain engineering is to produce a set of reusable assets for a family of systems, which are then used to build concrete members of the family. Flexibility is the ease with which a system or component can be modified for use in applications other than those for which it was originally designed. In this paper, we discuss how the MLaw infrastructure was designed to support interaction law evolution providing support to produce a set of reusable laws for a family of systems. As an example, we have implemented two customizable applications in the area of electronic negotiation expressed as an open system environment.

Resumo em Inglês:

In order to cope with the heterogeneity, autonomy and diversity of interests among the different agents in open multi-agent systems, several governance mechanisms have been defined. Governance mechanism enforce the behavior of agents by establishing a set of norms that describe actions that agents are prohibited, permitted or obligated to do. In this paper we present a governance mechanism that enforces not only dialogical actions but also non-dialogical ones. Although several governance mechanisms have been proposed, none of them satisfactorily deals with non-dialogical actions. Our proposed mechanism is based on testimonies provided by agents about the behavior of other agents. The governance mechanism provides decisions pointing out if norms have been violated or if false testimonies have been supplied. The decisions are based not only on testimonies and depositions provided by the agents but also on the agents’ reputations supplied by a reputation system that is part of the mechanism.

Resumo em Inglês:

The Semantic Web provides access to heterogeneous, distributed information, enabling software products to mediate between user needs and the information sources available. Agents are one of the most promising technologies for the development of Semantic Web software products. However, agent-based technologies will not become widespread until there are adequate infrastructures for the development of semantic multi-agent systems (MAS). Some challenges, such as turning software agents into practical abstractions for dealing with ontologies, taking advantage of the distributed nature of the Web to create distributed agents and making a seamless integration with existing Web tools, e.g. the browser, still need to be addressed. This paper describes the main features of the SemantiCore framework, an agent infrastructure to develop semantic MAS. A look at a benchmark Semantic Web application illustrates the SemantiCore potential as an infrastructure for the deployment of semantic agent applications.

Resumo em Inglês:

To facilitate the development of multi-agent systems and improve the reusability, robustness and feasibility of these systems, we have developed a role-based agent development framework (RADE). In this paper, we present an integrated approach for modeling, designing and implementing multi-agent systems using RADE. We describe the design of agents and motivations within such framework. We introduce a practical approach for modeling agent’s motivation and specifying agent’s goals, where a role-agent mapping mechanism is developed based on this design. Dynamic task allocation is achieved through the creation of role instances and the mapping from role instances to agents. We also introduce the RTÆMS language based on the extension of TÆMS to model the plan tree for each goal. This representation enables the reuse of general planning/scheduling and collaboration/cooperation mechanisms developed in multi-agent system research community. We have developed an automatic agent generation interface and also implemented a simple demo system in health care domain.

Resumo em Inglês:

The present paper describes a system for the construction of visual maps ("mosaics") and motion estimation for a set of AUVs (Autonomous Underwater Vehicles). Robots are equipped with down-looking camera which is used to estimate their motion with respect to the seafloor and built an online mosaic. As the mosaic increases in size, a systematic bias is introduced in its alignment, resulting in an erroneous output. The theoretical concepts associated with the use of an Augmented State Kalman Filter (ASKF) were applied to optimally estimate both visual map and the fleet position.