The learning of temporal patterns is a major challenge of Data mining. We introduce a temporal pattern model called Temporal Interval Tree Association Rules (Tita rules or Titar). This pattern model can be used to express both uncertainty and temporal inaccuracy of temporal events. Among other things, Tita rules can express the usual time point operators, synchronicity, order, and chaining,disjunctive time constraints, as well as temporal negation. Tita rules are designed to allow predictions with optimum temporal precision. Using this representation, we present the Titar learner algorithm that can be used to extract Tita rules from large datasets expressed as Symbolic Time Sequences. This algorithm based on entropy minimization, apriori pruning and statistical dependence analysis. We evaluate our technique on simulated and real world datasets. The problem of temporal planning with Tita rules is studied. We use Tita rules as world description models for a Planning and Scheduling task. We present an efficient temporal planning algorithm able to deal with uncertainty, temporal inaccuracy, discontinuous (or disjunctive) time constraints and predictable but imprecisely time located exogenous events. We evaluate our technique by joining a learning algorithm and our planning algorithm into a simple reactive cognitive architecture that we apply to control a robot in a virtual world.
