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This work presents a multiagent system for evaluating automatically the interaction that exists between the atmosphere and the ocean surface, monitoring and evaluating within the ocean carbon dioxide exchange process is a function requiring working with a great amount of data: satellite images and in situ Vessel’s data. The system presented in this work focuses on Ambient Intelligence (AmI) technologies since the vision of AmI assumes seamless, unobtrusive, and often invisible but also controllable interactions between humans and technology. The work presents the construction of an open multiagent architecture which, based on the use of deliberative agents incorporating Case-Based Reasoning (CBR) systems, offers a distributed model for such an interaction. This work also presents an analysis and design methodology that facilitates the implementation of CBR agent-based distributed artificial intelligent systems. Moreover, the architecture takes into account the fact that the working environment is dynamic and therefore it requires autonomous models that evolve overtime. In order to resolve this problem an intelligent environment has been developed, based on the use of CBR agents, which are capable of handling several goals, constructing plans from the data obtained through satellite images and research Vessels, acquiring knowledge, and of adapting to environmental changes, are incorporated. The artificial intelligence system has been successfully tested in the North Atlantic ocean, and the results obtained will be presented within this work.

This work presents a multiagent system for evaluating automatically the interaction that exists between the atmosphere and the ocean surface. Monitoring and evaluating within the Ocean CO2 exchange process is a function requiring working with a great amount of data: satellite images and in-situ vessel’s data. The system presented in this work focuses on Ambient Intelligence (AmI) technologies since the vision of AmI assumes seamless, unobtrusive, and often invisible but also controllable interactions between humans and technology. The work presents the construction of an open multiagent architecture which, based on the use of deliberative agents incorporating case-based planning (CBP) systems, offers a distributed model for such an interaction. This work also presents an analysis and design methodology that facilitates the implementation of CBR agent based distributed artificial intelligent systems. Moreover, the architecture takes into account the fact that the working environment is dynamic and therefore it requires autonomous models that evolve over time. In order to resolve this problem an intelligent environment has been developed, based on the use of CBP-CBR agents, which are capable of handling several goals, constructing plans from the data obtained through satellite images and research vessels, acquiring knowledge and of adapting to environmental changes, are incorporated. The artificial intelligence system has been successfully tested in the North Atlantic Ocean, and the results obtained will be presented within this work.

By improving accuracy in the quantification of the ocean’s CO2 budget, a more precise estimation can be made of the terrestrial fraction of global CO2 budget and its subsequent effect on climate change. First steps have been taken towards this from an environmental and economic point of view, by using an instance based reasoning system, which incorporates a novel clustering and retrieval method – a Cooperative Maximum Likelihood Hebbian Learning model (CoHeL). This paper reviews the problems of measuring the ocean’s CO2 budget and presents the CoHeL model developed and outlines the IBR system developed to resolve the problem.

Case-based reasoning systems, in general, and instance-based reasoning systems, in particular, are used more and more in industrial applications nowadays. During the last few years, researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a method for automating the retrieval stage and indexation of instance-based reasoning systems. This method is based on a modification of a new type of topology preserving map that can be used for scale invariant classifica- tion. The scale invariant map is an implementation of the negative feedback network to form a topology-preserving mapping. Maximum/minimum likelihood learning is applied in this paper to the scale invariant map and its possibilities are explored. This method automates the organization of cases and the retrieval stage of case-based reasoning systems. The proposed methodology groups instances with similar struc- ture, identifying clusters automatically in a data set in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.

A neuro-symbolic reasoning strategy for modelling a complex system is presented in which the aim is to forecast, in real time, the physical parameter values of a dynamic environment: the ocean. In situations in which the rules that determine a system are unknown the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation it has been found that a case-based reasoning system, in combination with an artifical neural network, can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The case-based reasoning system incorporates a radial basis function artificial neural network for the case adaptation. The results obtained from experiments, in which the system operated in real time in the oceanographic environment, are presented.

An approach to hybrid artificial intelligence problem solving is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. In situations in which the rules that determine a system are unknown or fuzzy, the prediction of the parameter values that determine the characteristic behavior of the system can be a problematic task. In such a situation, it has been found that a hybrid artificial intelligence model can provide a more effective means of performing such predictions than either connectionist or symbolic techniques used separately. The hybrid forecasting system that has been developed consists of a case-based reasoning system integrated with a radial basis function artificial neural network. The results obtained from experiments in which the system operated in real time in the oceanographic environment, are presented

An instance-based problem solving model is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. The situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation it has been found that an instance-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The instance-based reasoning system incorporates a radial basis function artificial neural network for the instance adaptation. The results obtained from experiments, in which the system operated in real time in the oceanographic environment, are presented.