The specification of a Multi-Agent System (MAS) involves the identification of a large number of entities and their relationships. This is a non-trivial task that requires managing different views of the system. Many problems concerning this issue originate in the presence of contradictory goals and tasks, inconsistencies, and unexpected behaviours. Such troublesome configurations should be detected and prevented during the development process in order to study alternative ways to cope with them. In this paper, we present methods and tools that support the management of contradictions during the analysis and design of MAS. Contradiction management in MAS has to consider both individual (i.e. agent) and social (i.e. organization) aspects, and their dynamics. Such issues have already been considered in social sciences, and more concretely in the Activity Theory, a social framework for the study of interactions in activity systems. Our approach applies knowledge from Activity Theory in MAS, especially its base of contradiction patterns. That requires a formalization of this social theory in order to be applicable in a software engineering context and its adaptation to agent-oriented methodologies. Then, it will be possible to check the occurrence of contradiction patterns in a MAS specification and provide solutions to those situations. This technique has been validated by implementing an assistant for the INGENIAS Development Kit and has been tested with several case studies. This paper shows part of one of these experiments for a web application.

Remote sensing technologies are experiencing a surge in adoption for monitoring Earth’s environment, demanding more efficient and scalable methods for image analysis. This paper presents a new approach for the Emirates Mars Mission (Hope probe); A serverless computing architecture designed to analyze images of Martian auroras, a key aspect in understanding the Martian atmosphere. Harnessing the power of OpenCV and machine learning algorithms, our architecture offers image classification, object detection, and segmentation in a swift and cost-effective manner. Leveraging the scalability and elasticity of cloud computing, this innovative system is capable of managing high volumes of image data, adapting to fluctuating workloads. This technology, applied to the study of Martian auroras within the HOPE Mission, not only solves a complex problem but also paves the way for future applications in the broad field of remote sensing.