A System for the Behaviour Analysis of Laboratory Animal Based on Qualitative Modelling

The paper presents a system LABAQM for the analysis of laboratory animal behaviour based on qualitative modelling. We are dealing with the cognitive phase of the laboratory animal behaviour analysis as a part of the pharmacological experiments. The system is based on the quantitative data from the tracking application and incomplete domain background knowledge. The LABAQM system operates in two main phases: behaviour learning and behaviour analysis. The learning and behaviour analysis phase are based on symbol sequences, obtained by the transformation of the quantitative data. Behaviour learning phase includes supervised learning procedure, unsupervised learning procedure and their combination. The combination of supervised and unsupervised learning procedures produces more robust models of characteristic behaviours which are used in the behaviour analysis phase. The Hidden Markov Model (HMM) has been used as a model of object behaviour, based on qualitative spatial and temporal representation and conceptual grouping method.