In multi-agent systems (MAS), agents can be governed by regulations. Due to an ever-evolving set of exogenous or endogenous changes, the ability of MAS to adapt regulations becomes crucial. In the MAS literature, there is a lack of comprehensive works defining models to adapt regulations. We propose a general regulation adaptation model for MAS that defines regulation adaptation representations (i.e., detect-fact, design-fact, and execute-fact) and regulation adaptation capabilities (i.e., detect, design, and execute). We also propose a method that uses constitutive and regulative norms together with the adaptation representations to govern the execution of the regulation adaptation capabilities. We illustrate the feasibility of our model by extending the SAI and NPL(s) normative engines to support regulation adaptation and integrating them into a normative agent architecture in the JaCaMo framework.

Hyper-heuristics are high-level methodologies responsible for automatically discover how to combine elements from a low-level heuristic set in order to solve optimization problems. Agents, in turn, are autonomous component responsible for watching an environment and perform some actions according to their perceptions. Thus, agent-based techniques seem suitable for the design of hyper-heuristics. This work presents an agent-based hyper-heuristic framework for choosing the best low-level heuristic. The proposed framework performs a cooperative voting procedure, considering a set of quality indicator voters, to define which multi-objective evolutionary algorithm (MOEA) should generate more new solutions along the execution.

Supply chain management (SCM) has increased its importance in the last decades, accordingly demanding new approaches to support its decision making processes. Simulation has been advised as an adequate approach for fulfilling such demand. However, develop monolithic simulation models representing the whole supply chain can be costly and time consuming. In the iron ore supply chain in which the seaports have the same features, the use of generic models and distributed simulation may be a real alternative in order to reduce the development time and costs. This paper presents a distributed simulation model of the maritime logistics in an iron ore supply chain applied to support fleet management decisions. Such model was used to perform an experiment in order to determine the maximum possible cargo volume supported by a ship fleet.

People rely on their network of relations, differentiating their contacts according to social groups, roles, knowledge and confidence, among other things. For an intelligent agent to be able to handle tasks on such a social environment a suitable model is needed, addressing the socio-organizational components. We detail such an approach, binding knowledge sharing, reputation, and organizations to allow an intelligent agent to explicitly express and manipulate a subjective view of the social environment of its human owner and how these skills can be applied to assembly a design team. After a presentation of the theoretical underpinning, details are given about the technological framework with a multi-agent system (MAS) dedicated to knowledge sharing. The constituent agents are described to show how they explore the options offered by our approach.

The authors discuss the implementation and run-time support for multi-agent systems (MAS). They start presenting MAS in the context of open distributed processing (ODP). Next, a model of a cognitive agent being currently developed at LIFIA is detailed. Taking this model as reference, the authors examine alternatives for supporting cognitive agents in distributed and heterogeneous environments. Finally, a distributed processing tool developed by the authors is presented. This tool follows the active object model and it is shown that active object and agent are strongly related concepts.