Journal article
Structural plan similarity based on refinements in the space of partial plans
Computational intelligence, Vol.33(4), pp.926-947
01 Nov 2017
Abstract
Plan similarity measures play a key role in many areas of artificial intelligence, such as case-based planning, plan recognition, ambient intelligence, or digital storytelling. In this paper, we present 2 novel structural similarity measures to compare plans based on a search process in the space of partial plans. Partial plans are compact representations of sets of plans with some common structure and can be organized in a lattice so that the most general partial plans are above the most specific ones. To compute our similarity measures, we traverse this space of partial plans from the most general to the most specific using successive refinements. Our first similarity measure is designed for propositional plan formalisms, and the second is designed for classical planning formalisms (including variables and types). We also introduce 2 novel refinement operators used to traverse the space of plans: an ideal downward refinement operator for propositional partial plans and a finite and complete downward refinement operator for classical partial plans. Finally, we evaluate our similarity measures in the context of a nearest neighbor classifier using 2 datasets commonly used in the plan recognition literature (Linux and Monroe), showing good results in both synthetic and real data.
Metrics
2 Record Views
Details
- Title
- Structural plan similarity based on refinements in the space of partial plans
- Creators
- Antonio A. Sanchez-Ruiz - Complutense University of MadridSantiago Ontanon - Drexel University
- Publication Details
- Computational intelligence, Vol.33(4), pp.926-947
- Publisher
- Wiley
- Number of pages
- 22
- Grant note
- IIS-1551338 / National Science Foundation; National Science Foundation (NSF) TIN2014-55006-R / Spanish Ministry of Economy, Industry and Competitiveness
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Identifiers
- 991019167989904721
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence