Mining Temporal Patterns from Relational Data

Agents in dynamic environments have to deal with world representations that change over time. In order to allow agents to act autonomously and to make their decisions on a solid basis an interpretation of the current scene is necessary. If intentions of other agents or events that are likely to happen in the future can be recognized, the agent’s performance can be improved as it can adapt the behavior to the situation. In this work we present an approach which applies unsupervised symbolic learning off-line to a qualitative abstraction in order to create frequent temporal patterns in dynamic scenes. Here, an adaption of a sequential pattern mining algorithm which was presented earlier by the authors is proposed in order to reduce the complexity by handling different aspects (class restrictions, variable unifications, and temporal relations) separatedly first, and then combining the results of the single steps. The work is still in progress– this paper introduces the basic ideas and shows an example run of the implemented system.