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This paper proposes a new general approach based on Bayesian networks to model the human behaviour. This approach represents human behaviour withprobabilistic cause-effect relations based not only on previous works, but also with conditional probabilities coming either from expert knowledge or deduced from observations. The approach has been used in the co-simulation of building physics and human behaviour in order to assess the CO 2 concentration in an office.
This paper analyzes two modeling approaches for occupant behaviour in buildings. It compares a purely statistical approach with a multi-agent social simulation based approach. The study concerns the door openings in an office.
Crowd models can be used for the simulation of people movement in the built environment. Crowd model outputs have been used for evaluating safety and comfort of pedestrians, inform crowd management and perform forensic investigations. Microscopic cro
Reasoning in a temporal knowledge graph (TKG) is a critical task for information retrieval and semantic search. It is particularly challenging when the TKG is updated frequently. The model has to adapt to changes in the TKG for efficient training and
With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students knowledge status and predicts their performance on new questions. Questions are often numerous in online education systems,
There is a significant lack of unified approaches to building generally intelligent machines. The majority of current artificial intelligence research operates within a very narrow field of focus, frequently without considering the importance of the