We propose a memory-based approach to the problem of goal-schema recognition. We use a generic episodic memory module to perform incremental goal schema recognition and to build the plan library. Unlike other case-based plan recognizers it does not require complete knowledge of the planning domain or the ability to record intermediate planning states. Similarity of plans is computed incrementally using a semantic matcher that considers the type and parameters of the observed actions. We evaluate this approach on two datasets and show that it is able to achieve similar or better performance compared to a statistical approach, but offers important advantages: plan library is acquired incrementally and the memory structure it builds is multifunctional and can be used for other tasks such as plan generation or classification.

@InProceedings{tecuci-flairs09,
  author = 	 {Dan Tecuci and Bruce Porter},
  title = 	 {Memory {B}ased {G}oal {S}chema {R}ecognition},
  note =         {{P}roceedings to the 22nd Florida Artificial Intelligence Research Society {C}onference ({FLAIRS}22) },
  year =         {2009},
  address =      {Sanibel Island, {FL}}
  }