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Abstract-A mult i-agent system (MAS) is formed by a number of agents connected together to achieve the desired goals specified by the design. Usually in a mu lti agent system, agents work on behalf of a user to accomplish given goals. In MAS co-ordination, co-operation, negotiation and communication are important aspects to achieve fault tolerance in MAS. The mu lti-agent system is likely to fail in a distributed environment and as an outcome o f such, the resources for MAS may not be available due to the failure of an agent, mach ine crashes, process failure, software failure, communicat ion failure and/or hardware failure. Therefore, many researchers have proposed fault tolerance approaches to overcome the failure in MAS. So we have surveyed these approaches in this paper, whereby our contribution is threefold. Firstly, we have provided taxono my of faults and techniques in MAS. Secondly, we have provided a qualitative comparison of existing fault tolerance approaches. Thirdly, we have provided an evaluation of existing fau lt tolerance techniques. Results show that most of the existing schemes are not very efficient, due to various reasons like high computation costs, costly replication and large co mmunicat ion overheads.

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This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.

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