An Agent-based Decision Support for a Vaccination Campaign

We explore the Covid-19 diffusion with an agent-based model of an Italian region with a population on a scale of 1:1000. We also simulate different vaccination strategies. From a decision support system perspective, we investigate the adoption of artificial intelligence techniques to provide suggestions about more effective policies. We adopt the widely used multi-agent programmable modeling environment NetLogo, adding genetic algorithms to evolve the best vaccination criteria. The results suggest a promising methodology for defining vaccine rates by population types over time. The results are encouraging towards a more extensive application of agent-oriented methods in public healthcare policies

simulation for change management an industrial application

Abstract This paper describes an application of change management in the context of a growing company: the ABC enterprise. The first step of the proposed methodological framework involves the construction of the As-is process model, adopting the standard BPMN language. The model is based on an accurate analysis of the data concerning the resources and activities of the company being analyzed, in order to perform a computational simulation of its business processes. After examining existing solutions for business challenges and technological opportunities, several scenarios can be proposed that include possible changes to existing processes. By simulating these scenarios, the results can suggest to analysts useful information to evaluate possible restructuring actions in a quantitative way, comparing the values of an appropriate set of indicators before and after the model's restructuring

ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm

This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figurative Language in Twitter, at SemEval 2015. Our system used a regression model and additional external resources to assign polarity values. A distinctive feature of our approach is that we used not only word-sentiment lexicons providing polarity annotations, but also novel resources for dealing with emotions and psycholinguistic information. These are important aspects to tackle in figurative language such as irony and sarcasm, which were represented in the dataset. The system also exploited novel and standard structural features of tweets. Considering the different kinds of figurative language in the dataset our submission obtained good results in recognizing sentiment polarity in both ironic and sarcastic tweets