Partial plan recognition with incomplete information

Explores the benefits of using user models for plan recognition problems in a real-world application. Self-interested agents are designed for the prediction of resource usage in the UNIX domain using a stochastic approach to automatically acquire regularities of user behavior. Both sequential information from the command sequence and relational information such as system´s responses and arguments to the commands are considered to typify a user´s behavior and intentions. Issues of ambiguity, distraction and interleaved execution of user behavior are examined and taken into account to improve the probability estimation in hidden Markov models.