Name
Abstract | ||
|---|---|---|
We present a novel approach to enable a mobile service robot to understand questions about the history of tasks it has executed. We frame the problem of understanding such questions as grounding an input sentence to a query that can be executed on the logs recorded by the robot during its runs. We define a query as an operation followed by a set of filters. In order to ground sentence to a query we introduce a joint probabilistic model. The model is composed by a shallow semantic parser and a knowledge base to store and re-use the groundings of a sentence. The Knowledge Base and its predicates are designed to match the structure of a query. Our results show that, by using such Knowledge Base, the approach proposed requires fewer and fewer corrections as users interact with the system. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ROMAN.2017.8172318 | 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) |
Keywords | Field | DocType |
task history,mobile service robot,joint probabilistic model,shallow semantic parser,knowledge base,query sentence | Computer vision,Information retrieval,Computer science,Knowledge-based systems,Natural language,Artificial intelligence,Parsing,Knowledge base,Robot,Sentence,Semantics,Service robot | Conference |
ISSN | ISBN | Citations |
1944-9445 | 978-1-5386-3519-3 | 1 |
PageRank | References | Authors |
0.37 | 17 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vittorio Perera | 1 | 32 | 5.34 |
| Manuela Veloso | 2 | 8563 | 882.50 |