José Alberto R. P. Sardinha

An Object-Oriented Framework for Building Software Agents

Agent technology is a new approach of Distributed Artificial Intelligence to implement autonomous entities driven by beliefs, goals, capabilities and plans, and other agency properties such as adaptation, interaction, and mobility. Software agents are the focus of considerable research in the artificial intelligence community, but not much has been done in the field of software engineering. In this paper, we present an object-oriented framework for building software agents in a distributed environment. The design of the framework also allows an easy mapping of the models developed in the analysis and design phase of the Gaia Methodology to object-oriented code. We believe that objectoriented framework technology can reduce not only the development time but also the complexity of implementing multi-agent systems. We present an instantiated application that uses this framework to illustrate an implementation.

A combined specification language and development framework for agent-based application engineering

Abstract Software agents are used to solve several complex problems. Software engineering processes and tools are vital to support all the development phases of agent-based systems, and guarantee that these systems are built properly. Indeed, many modeling languages and implementation frameworks are available for system engineers. However, there are few methods that properly combine agent-oriented design and implementation initiatives. In this paper, we propose a development method that goes from the requirements elicitation to the actual implementation of agent systems using a modeling language and an implementation framework. We also present a case study to illustrate the suitability of our approach.

A systematic approach for including machine learning in multi-agent systems

Large scale multi-agent systems (MASs) in unpredictable environments must use machine learning techniques to perform their goals and improve the performance of the system. This paper presents a systematic approach to introduce machine learning in the design and implementation phases of a software agent. We also present an incremental implementation process for building asynchronous and distributed agents, which suppors the combination of machine learning strategies. This process supports the stepwise building of adaptable MASs for unknown situations, improving their capacity to scale up. We use the Trading Agent Competition (TAC) environment as a case study to illustrate the suitability of our approach.

Software engineering for large-scale multi-agent systems - SELMAS'2003

Objects and agents are abstractions that exhibit Points of similarity, but the development of multi-agent systems (MASs) poses other challenges to Software Engineering since software agents are inherently more complex entities. In addition, a large MAS needs to satisfy multiple stringent requirements such as reliability, trustability, security, interoperability, scalability, reusability, and maintainability. This workshop brings together researchers and practitioners to discuss the current state of the art and the future research directions in software engineering for large-scale MASs. A particular interest is to understand those issues in the agent technology that make it difficult and/or improve the production of complex distributed systems.