Path planning on gridmaps is a common problem in AI and a popular topic in application areas such as computer games. Compressed Path Databases (CPDs) represent a state-of-theart approach to the problem, in terms of the speed of computing full optimal paths and also individual optimal moves. Despite significant improvements in recent years, the memory required to store a CPD can still be a bottleneck for large game maps. In this work we present a new compression approach that can reduce the size of CPDs. Our approach uses an extended notion of wildcards and a novel concept called a redundant symbol. We implement our ideas on top of a state-of-the-art CPD system and, in a range of experiments, we demonstrate a substantial reduction in the size of CPDs.

During the COVID-19 pandemic, the corporate online training sector has increased exponentially and online course providers had to implement innovative solutions to be more efficient and provide a satisfactory service. This paper considers a real case study in implementing a chatbot, which answers frequently asked questions from learners on an Italian e-learning platform that provides workplace safety courses to several business customers. Having to respond quickly to the increase in the courses activated, the company decided to develop a chatbot using a cloud-based service currently available on the market. These services are based on Natural Language Understanding (NLU) engines, which deal with identifying information such as entities and intentions from the sentences provided as input. To integrate a chatbot in an e-learning platform, we studied the performance of the intent recognition task of the major NLU platforms available on the market with an in-depth comparison, using an Italian dataset provided by the owner of the e-learning platform. We focused on intent recognition, carried out several experiments and evaluated performance in terms of F-score, error rate, response time, and robustness of all the services selected. The chatbot is currently in production, therefore we present a description of the system implemented and its results on the original users’ requests.

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