Multiagent and Grid Systems

Our everyday environments are going to be disseminated of devices and sensors that exhibit some degree of autonomy and that collaborate to support users in their activities. This envisions a not-so-far future called “Ambient Intelligence”, i.e., environments that show a certain degree of intelligence. Useful abstractions for Ambient Intelligence are represented by software agents, since they are autonomous and capable of acting on behalf of their owners. In the context of the LAICA project we have exploited the agent paradigm to model devices and sensors, and have developed an ad-hoc middleware to orchestrate all the involved components. This paper discusses the adoption of agents in Ambient Intelligence, and explains the design and implementation of the middleware.

In recent years, the mobile agent paradigm has drawn a lot of attention because of its attractive features, such as autonomy, mobility, flexibility, adaptability, efficiency, etc. Unfavorable outcomes may occur, however, if an agent only partially fulfills its tasks because of the limitations or restrictions of the environment. Transaction mechanisms can be used to prevent unfavorable outcomes. There are two fundamental building blocks, locking and rollback, that can be used in transaction mechanisms. In this paper, we propose a framework, called Dependent Partial Rollback, to apply the flexible transaction model and partial rollback to mobile agent systems. We analyse the performances of several transaction mechanisms in the mobile agent paradigm and show that the Dependent Partial Rollback should be the most efficient under certain conditions. Furthermore, we demonstrate the power of the Dependent Partial Rollback framework with a simple e-marketplace implementation.

Process calculi are mathematical tools used for modeling and analyzing the structure and behavior of reactive systems. One such calculus, called APi-calculus (an extension to Pi-calculus), provides extensive support for modeling migration, intelligence, natural grouping and security in agent-based systems [18]. In this article, a visualization tool for APi-calculus is proposed. Although an invaluable formal method for mobile agent systems, APi-calculus is quite complex (as are other calculi) and not very well suited to working with practical applications by itself. Due to the dynamic nature of mobile agents, a graphical program based on the mathematics of the calculus would enhance the use of the calculus and its attraction to the mobile agents industry. The ability to provide instant demonstration of a designed system to the user and the support for verification and validation of such systems specified by the calculus would prove to be a great asset for APi-calculus as well as agents-based computing in general. This paper presents ACVisualizer, a visualization software for APi-calculus, which provides such high level support for modeling mobile agent systems. Due to the backward compatibility of APi-calculus with Pi-calculus, ACVisualizer can easily be adapted to work with Pi-calculus itself.

This paper presents approaches for building, managing, and evaluating consensus ontologies from the individual ontologies of a network of socially interacting agents. Each agent has its own conceptualization of the world within the multiagent system framework. The interactions between agents are modeled by sending queries and receiving responses and later assessing each other’s performance based on the results. This model enables us to measure the quality of the societal beliefs in the resources which we represent as the expertise in each domain. The dynamic nature of our system allows us to model the emergence of consensus that mimics the evolution of language. We present an algorithm for generating the consensus ontologies which makes use of the authoritative agent’s conceptualization in a given domain. As the expertise of agents changes after a number of interactions, the consensus ontology that we build based on the agents’ individual views evolves. The resulting approach is concordant with the principles of emergent semantics. We provide formal definitions for the problem of finding a consensus ontology in a step by step manner. We evaluate the consensus ontologies by using different heuristic measures of similarity based on the component ontologies. Conceptual processing methods for generating, manipulating, and evaluating consensus ontologies are given and experimental results are presented. The presented approach looks promising and opens new directions for further research.

This paper shows a searching mechanism to discover Semantic Web Services satisfying client requirements. The increase in web services and lack of semantic base in search mechanisms of UDDI make it difficult for clients to find a required web service. We developed a system which uses a Semantic Search Agent (SSA) to discover suitable web services according to client requirements then presents them as a result page. The system uses Ontology Web Language for Services (OWL-S), which allows semantic description of web services. Hence, the SSA is able to understand predefined features of Semantic Web Services, extract necessary information, and decide on which services would meet the service requirement of a client. The system retargets and combines complimentary aspects of two research topics (Smart Web Query Engine and Matchmaking Algorithm of OWL-S/UDDI Matchmaker) to facilitate the provision of web services to a client.

The newly envisioned Semantic Web contains as a component what may be called a Web of Trust, consisting of the trust relationships held between its participants. In terms of this, the degree to which participant A believes what is believed by participant B depends on the degree to which A trusts B. This paper presents a formal language with well-defined semantics within which such a participant (or agent) can express the relevant conditions of belief and trust, and outlines some key techniques for reasoning with these expressions. Novel in this treatment is the use of linguistic, rather than numeric, measures of belief. The aim in this is to make the language and reasoning system more intuitive for the human user. Also novel is an explicit delineation of the context within which the language and reasoning techniques are to be applied. The notion of a dynamic reasoning system is applied to model the agent’s knowledge acquisition and belief revision processes as activities that take place in time.