Case-Based Plan Recognition Using Action Sequence Graphs
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2014
- Accession Number
- ADA616509
Entities
People
- David W. Aha
- Michael Floyd
- Swaroop S. Vattam
Organizations
- United States Naval Research Laboratory