794 citations · 5.7k across the 143 of their papers we have counts for
4 papers · 1 filter
Learning STRIPS Operators from Noisy and Incomplete Observations
Kira Mourao, Luke S. Zettlemoyer, Ronald P. A. Petrick +1
Agents learning to act autonomously in real-world domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, esp…
Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
Luke S. Zettlemoyer, Michael Collins
This paper addresses the problem of mapping natural language sentences to lambda-calculus encodings of their meaning. We describe a learning algorithm that takes as input a trainin…
A Joint Model of Language and Perception for Grounded Attribute Learning
Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer +2
As robots become more ubiquitous and capable, it becomes ever more important to enable untrained users to easily interact with them. Recently, this has led to study of the language…
Learning Probabilistic Relational Dynamics for Multiple Tasks
Ashwin Deshpande, Brian Milch, Luke S. Zettlemoyer +1
The ways in which an agent's actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of lea…