Solving Interpretation Problems With Etcetera Abduction (bibtex)
by Andrew S Gordon
Abstract:
Among the most challenging problems in Artificial Intelligence are those that require human-like abilities to make sense of ambiguous observations, to interpret events in context given a wealth of life experiences and commonsense knowledge. In the 1990s, Jerry Hobbs and colleagues demonstrated how interpretation problems can be tackled with logical abduction, a combinatorial search for the best set of assumptions that logically entails the observations. Etcetera Abduction is a new approach to ranking assumptions by reifying the uncertainty of knowledge base axioms as etcetera literals, representing conditional and prior probabilities that can be combined through logical unification. In this invited talk, I will highlight some of the features of Etcetera Abduction that make it attractive compared to alternatives, and share my perspective on the role of logic-based reasoning given current trends in machine learning research.
Reference:
Solving Interpretation Problems With Etcetera Abduction (Andrew S Gordon), In Proceedings of the Fifth Annual Conference on Advances in Cognitive Systems, 2014 Cognitive Systems Foundation, 2017.
Bibtex Entry:
@inproceedings{gordon_solving_2017,
	address = {Troy, New York},
	title = {Solving {Interpretation} {Problems} {With} {Etcetera} {Abduction}},
	url = {http://people.ict.usc.edu/~gordon/publications/ACS17.PDF},
	abstract = {Among the most challenging problems in Artificial Intelligence are those that require human-like abilities to make sense of ambiguous observations, to interpret events in context given a wealth of life experiences and commonsense knowledge. In the 1990s, Jerry Hobbs and colleagues demonstrated how interpretation problems can be tackled with logical abduction, a combinatorial search for the best set of assumptions that logically entails the observations. Etcetera Abduction is a new approach to ranking assumptions by reifying the uncertainty of knowledge base axioms as etcetera literals, representing conditional and prior probabilities that can be combined through logical unification. In this invited talk, I will highlight some of the features of Etcetera Abduction that make it attractive compared to alternatives, and share my perspective on the role of logic-based reasoning given current trends in machine learning research.},
	booktitle = {Proceedings of the {Fifth} {Annual} {Conference} on {Advances} in {Cognitive} {Systems}},
	publisher = {2014 Cognitive Systems Foundation},
	author = {Gordon, Andrew S},
	month = may,
	year = {2017},
	keywords = {Narrative}
}
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