Computational intelligence based forecasting approaches proved to be more efficient in real time air pollution forecasting systems than the deterministic ones that are currently applied. Our research main goal is to identify the computational intelligence model that is more proper to real time PM2.5 air pollutant forecasting in urban areas. Starting from the study presented in [27]a, in this paper we first perform a comparative study between the most accurate computational intelligence models that were used for particulate matter (fraction PM2.5) air pollution forecasting: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). Based on the obtained experimental results, we make a comprehensive analysis of best ANN architecture identification. The experiments were realized on datasets from the AirBase databases with PM2.5 concentration hourly measurements. The statistical parameters that were computed are mean absolute error, root mean square error, index of agreement and correlation coefficient.

Many real-world applications are mapped into combinatorial problems. An example of such problem is timetable scheduling. In this case, the two basic characteristics can be defined by its distributed and dynamic environment. One efficient solution to solve this problem could be provided by an agent-based approach. A timetable scheduling problem can be modelled as a multi-agent system that provides the final schedule by taken into account all the restrictions. In this paper it is presented a preliminary research work that involves the development of a multi-agent system for university course timetable scheduling, named MAS_UP-UCT. We focus on the architecture of the multi-agent system, and on the evaluation of the communication process by using the interaction diagrams.

Multi-robot systems can be used in various industrial and non-industrial applications such as manufacturing, environmental monitoring, disaster rescue missions or agricultural foraging. In this context, different problems need to be solved: robot control, robot perception, multi-robot system coordination, global and local planning/re-planning etc. For example, choosing a good coordination mechanism is a difficult task that depends on the application and can be done via trial and error experiments and comparative studies. Some guidelines can be learned from the case studies reported in the literature. Moreover, artificial intelligence provides several techniques (e.g. rule-based systems, machine learning, artificial neural networks, swarm intelligence) that can improve the performances of multi-robot systems coordination. Regarding experimental comparative studies between different methods, a cheaper solution is given by simulations. On the other hand, knowledge sharing between robots can be provided by ontologies that facilitate the inter-robot communication as a base of multi-robot system coordination. Our research work focus on modelling a multi-robot system as an agent based system which is a proper choice for distributed systems and enables simulations. In this paper we present an agentbased model for multi-robot systems that use a common ontology and reinforcement learning as agent adaptation ability. An application for environmental quality monitoring is discussed.

Air pollution with suspended particles from PM_2.5 fraction represents an important factor to increasing atmospheric pollution degree in urban areas, with a significant potential effect on the health of vulnerable people such as children and elderly. PM_2.5 air pollutant concentration continuous monitoring represents an efficient solution for the environment management if it is implemented as a real time forecasting system which can detect the PM_2.5 air pollution trends and provide early warning or alerting to persons whose health might be affected by PM_2.5 air pollution episodes. The forecasting methods for PM concentration use mainly statistical and artificial intelligence-based models. This paper presents a model based protocol, MBP – PM _2.5 forecasting protocol, for the selection of the best ANN model and a case study with two artificial neural network (ANN) models for real time short-term PM_2.5 forecasting.

The paper presents a methodological framework, ABVE-Frame, for the development of virtual enterprises based on intelligent agents that are implemented as a simulation. The proposed framework combines an agent-based approach with an ontological approa