IESM 2015 - International Conference on Industrial Engineering and Systems, Séville, ESPAGNE, 21-/10/2015 - 23/10/2015Reaching the critical software safety requirements is one of the most important and complex tasks for the safety-related industry. This fact explains, as it was highly recommended by the CENELEC standard, the increasing use of formal means in the development process. However, industrial environments are still reticent facing difficulties in incorporating those formal methods in a larger scale of application, especially because of their mathematical modeling complexity. The present paper proposes a Petri Nets-based approach for safety critical software development using a formal transformation into B abstract machines. This work presents formal definitions for the translation of Colored Petri Nets to B abstract machines. As part of the French research project called 'PERFECT', it aims at enabling a stronger combination of formal design techniques and analysis tools in order to cope with the real complexity of critical software development and to prove in an automated manner that the final software product satisfies all safety requirements. Therefore, the use of the B method will broaden the scope of its applicability by providing a new input modeling alternative. An illustrative application of the transformation practical use is shown in this paper for a railway level-crossing case study

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Artificial Intelligence (AI) and data are reshaping organizations and businesses. Human Resources (HR) management and talent development make no exception, as they tend to involve more automation and growing quantities of data. Because this brings implications on workforce, career transparency and equal opportunities, overseeing what fuels AI and analytical models, their quality standards, integrity and correctness becomes an imperative for those aspiring to such systems. Based on an ontology transformation to B-machines, this paper presents an approach to constructing a valid and error-free career agent with Deep Reinforcement Learning (DRL). In short, the agent's policy is built on a framework we called Multi State-Actor (MuStAc) using a decentralised training approach. Its purpose is to predict both relevant and valid career steps to employees, based on their profiles and company pathways (observations). Observations can comprise various data elements such as the current occupation, past experiences, performance, skills and qualifications, etc. The policy takes in all these observations and outputs the next recommended career step, in an environment set as the combination of a HR ontology and an Event-B model, which generates action spaces with respect to formal properties. The Event-B model and formal properties are derived using OWL to B transformation.

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