@inproceedings{0882ab2424ce4771b7b31050cab02a0d,
title = "An empirical characterization of parsimonious intention inference for cognitive-level imitation learning",
abstract = "Imitation learning is a promising route to better collaboration between humans and artificial agents. It will be most effective if the agent has some cognitive-level “understanding” of a human demonstrator{\textquoteright}s intentions. Inferring intent is an example of abductive reasoning, wherein an agent explains the available evidence based on causal knowledge. Good explanations should satisfy some notion of parsimony (“Occam{\textquoteright}s razor”), but the optimal notion of parsimony is often application-specific. We compare several such notions in the context of intention inference, using a robotic imitation learning scenario and the Monroe County Corpus, a standard benchmark in intention inference. Our results suggest that the most popular notions of parsimony in general are not necessarily appropriate for intention inference in particular.",
keywords = "Artificial intelligence, Imitation learning, Intention inference, Parsimonious covering theory",
author = "Garrett Katz and Huang, {Di Wei} and Rodolphe Gentili and James Reggia",
year = "2017",
month = jan,
day = "1",
language = "English (US)",
series = "2017 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2017 - Proceedings of the 2017 International Conference on Artificial Intelligence, ICAI 2017",
publisher = "CSREA Press",
editor = "Arabnia, {Hamid R.} and {de la Fuente}, David and Kozerenko, {Elena B.} and Olivas, {Jose A.} and Tinetti, {Fernando G.}",
booktitle = "2017 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2017 - Proceedings of the 2017 International Conference on Artificial Intelligence, ICAI 2017",
note = "2017 International Conference on Artificial Intelligence, ICAI 2017 at 2017 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2017 ; Conference date: 17-07-2017 Through 20-07-2017",
}