Accession Number : ADA616509


Title :   Case-Based Plan Recognition Using Action Sequence Graphs


Descriptive Note : Conference paper


Corporate Author : NAVAL RESEARCH LAB WASHINGTON DC NAVY CENTER FOR APPLIED RESEARCH IN ARTIFICIAL INTELLIGENCE


Personal Author(s) : Vattam, Swaroop S ; Aha, David W ; Floyd, Michael


Full Text : http://www.dtic.mil/get-tr-doc/pdf?AD=ADA616509


Report Date : Oct 2014


Pagination or Media Count : 16


Abstract : We present SET-PR, a novel case-based plan recognition algorithm that is tolerant to missing and misclassified actions in its input action sequences. SET-PR uses a novel representation called action sequence graphs to represent stored plans in its plan library and a similarity metric that uses a combination of graph degree sequences and object similarity to retrieve relevant plans from its library. We evaluated SET-PR by measuring plan recognition convergence and precision with increasing levels of missing and misclassified actions in its input. In our experiments, SET-PR tolerated 20%-30% of input errors without compromising plan recognition performance.


Descriptors :   *ARTIFICIAL INTELLIGENCE , *REASONING , *RECOGNITION , ALGORITHMS , CONVERGENCE , ERRORS , GRAPHS , PLANNING , PRECISION , SEQUENCES , SYMPOSIA , TOLERANCE


Subject Categories : Numerical Mathematics


Distribution Statement : APPROVED FOR PUBLIC RELEASE