Learning bayesian networks in semi-deterministic systems
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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In this thesis, we present a solution to the problem of discovering rules from sequential data. As part of the solution, the Temporal Investigation Method for Enregistered Record Sequences (TIMERS) and its implementation, the TimeSleuth software, are introduced. TIMERS uses the passage of time between attribute observations as justification for judging the causality of a rule set. Given a sorted sequence of input data records, and assuming that the effects take time to manifest themselves, we merge the input records to bring potential causes and effects together in the same record. Three tests are performed using three different assumptions on the nature of the relationship: instantaneous, causal, or acausal. The temporal reversibility of a relationship in time is used to judge the relationship as potentially acausal, while reversibility is considered as evidence for judging the relationship as potentially causal. To visualise the attributes' influence on each other, the thesis introduces dependence diagrams, which are graphs that connect condition attributes to decision attributes. We performed a series of comparisons between TIMERS and other causality discoverers, and also experimented with both synthetic and real temporal data for the discovery of temporal rules. The results show an improvement in the quality of the rules discovered with TIMERS.