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Abstract. Decision-making, while performed by humans, is also expected to be found in most (artificial) intelligent systems. Usually, the cognitive research assumption is that the individual is the correct unit for the analysis of (human) intelligence. Yet, the multi-agent assumption is that of a society of interacting individuals (agency) that collectively supersedes individual capabilities. Therefore, the entire society of agents is, itself, an additional locus for the analysis of this collective-intelligence. In this paper we propose models that explore the agent-agency mutual influence from the decision-making perspective. We outline three case study scenarios used during the model's experimental evaluation: i) large-scale disasters, ii) electricity markets, and iii) Web-empowered knowledge and social connectivity. The scenario-driven evaluations are being used to provide the alignment weights for the effort along our research lines.

This paper presents a simulation platform for control and monitor the Electric Vehicle charging process, based on existing power distribution limitations and Microgeneration capacity. The goal of this research is to simulate the energy consumption and their unexpected behavior, using past experience and taking into account distribution network and home power limitation to find an intelligent charging pattern. This paper proposes a novel approach for this problem based on a simulation platform where stochastic process is adopted to perform unexpected user behavior. This simulation platform can be used to determine the capability of the actual electrical distribution network for supplying energy to the final consumers and for charging the bank of batteries of electrical vehicles, which can occur simultaneously.

In this paper, we present a review on the use of ontologies in learning object repositories systems for searching and suggestion purposes, considering its adoption for the seaThings project that aims to promote the ocean literacy. We also describe the use case of the Cognix system and Agent-based Learning Objects -OBAA metadata standard for learning objects which is being implemented on a new learning objects repository. This system includes concepts from artificial intelligence such as agents and ontologies that aim to improve the search and so making the system more responsive. This paper also sugests how an ontology can be implemented, using metadata in learning object repositories to provide relevant aspects, such as interoperability, reuse, and searching.

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