Baldoino Fonseca dos Santos Neto

An architectural model for autonomous normative agents

Social norms have become one of the most promising approaches that use an informal social control to ensure a desirable social order in open multi-agent systems. In these systems, autonomous and heterogeneous entities work towards similar or different goals. Norms regulate the behaviour of agents by defining obligations and prohibitions, and by creating rewards and penalties to encourage the agents to meet theses constraints. The development of autonomous normative agents, i. e., agents able to take decisions by following their motivations related to their goals and taking into account the system norms, has proven to be significantly more challenging than the design of traditional agents. In this paper, we introduce an architectural model that provides a set of functions to support the agent on the reasoning about the norms. These operations assist the agent to perceive the systems norms, detect the fulfillment and violation of the norms while verifying their activation and deactivation, select the norms the agent intends to fulfill, identify and overcome conflicts among norms.

Developing Goal-Oriented Normative Agents: The NBDI Architecture

In open multi-agent systems norms are mechanisms used to restrict the behaviour of agents by defining what they are obligated, permitted or prohibited to do and by stating stimulus to their fulfillment such as rewards and discouraging their violation by pointing out punishments. In this paper we propose the NBDI architecture to develop goal-oriented normative agents whose priority is the accomplishment of their own desires while evaluate the pros and cons associated with the fulfillment or violation of the norms. The BDI architecture is extended by including norms related functions to check the incoming perceptions (including norms), select the norms they intend to fulfill based on the benefits they provide to the achievement of the agent‘s desires and intentions, and decide to cope or not with the norms while dropping, retaining or adopting new intentions. The applicability of our approach is demonstrated through an non-combatant evacuation scenario implemented by using the Normative Jason platform.

Chapter 2 - A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems

Self-organizing systems have increased in relevance and are used to deal with complex problems domains. They allow the development of decentralized solutions that exhibit a dynamic and adaptable behavior that can face the challenge of handling disturbances that were previously unknown in the environment. The main difficulties in building self-organizing multiagent systems lie in the lack of mechanisms to regulate the interaction and coordination between the agents of the environment and the reuse of already adopted solutions. In this context, this chapter proposes a framework as a reusable solution for building decentralized self-organizing systems, based on major architectural patterns found in the literature but only expressed at a high level of abstraction. It also provides extensibility features to develop new interaction and coordination mechanisms. In order to validate the framework, a self-organizing and decentralized solution to the automated guided vehicles problem was implemented.

Using Jason to Develop Refactoring Agents

Refactoring is one of the main techniques used when maintaining and evolving the software. It works by changing the software in such way that improves its internal structure without changing its external behavioral. This paper focuses on issues around software refactoring, such as: (i) figure out where the software should be refactored, (ii) define which refactoring(s) should be applied, (iii) ensure that the external behavior of the software will be preserved after applying the refactoring, (iv) evaluate the gains and losses on the software quality resulting of the refactoring, (v) apply the refactoring, and, (vi) maintain the consistence between the refactored program code and other artifacts. Given the amount of issues to be considered, the refactoring activities when done in a manual way are an error-prone and extremely expensive. This paper provides an extension of the Jason platform to enable the development of refactoring agents able to perform software refactoring in an autonomic way. Such approach accelerates the process of executing a refactoring and reduces the probability of introducing defects.