The system receives a pattern sequence, i.e., a time-series of
consecutive patterns as an input sequence. The set of input sequences
are given as a training set, where a category is attached to each input sequence, and a supervised learning is introduced. First, we introduce a state transition model, AST(Abstract State Transition), where the information of speed of moving objects is added to a state transition model. Next, we extend it to the model including a reinforcement learning, because it will be more powerful to learn
the sequence from the start to the goal. Last, we extend it to the model of state including a kind of pushdown tape that represents a knowledge behavior, which we call Pushdown Markov Model. The learning procedure is similar to the learning in MDP(Markov Decision Process) by using DP (Dynamic Programming) matching. As a result, we show a reasonable learning-based recognition of a trajectory for human behavior.