Nuclear accidents suchasthat at Chemobyl in 1986 have emphasised the need for improving the emergency management of any aceidentat release of radio-activity. RODOS isareal-time on-line decision support system intended tobe used throughout all phases of a nuclear accident. It follows a consistent Bayesian methodology for handling uncertainty and the effective communication of this to the decision makers. Evaluation is based upon multi-attribute value and utility methods with extensive provision of sensitivity analysis and automated explanations to the decision maker.
Figure 1: Pipeline for detecting human-defined social attitudes, including immersive data collection (user interaction (A) and expert annotating (B)) for training the machine learning model. This takes place by pre-training the model, creating Generative Adversarial Imitation Learning (GAIL) rewards for the reinforcement learning algorithm Proximal Policy Optimisation (PPO) that also uses a temporal memory called Long Short-Term Memory (LSTM) algorithm (C). This process exports a trained ML model (D). In a user-VC interaction (E), the trained model (F) detects in real time the human-defined social attitude (G) which could be used in different scenarios.
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