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Shawn Squire

dblp:138/5591 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
3 papers
Reinforcement learning · 76% Planning, search and constraint satisfaction · 16% Knowledge representation and reasoning · 9%

Topics — the 9 heaviest of 10, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
hierarchical reinforcement learning		 0.722020
Planning with Abstract Learned Models While Learning Transferable Subtasks · AAAI 2020
Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes · IJCAI 2015
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning under uncertainty
probabilistic planning		 0.412020
Planning with Abstract Learned Models While Learning Transferable Subtasks · AAAI 2020
Machine learning › Reinforcement learning
markov decision process		 0.322016
Abstracting Complex Domains Using Modular Object-Oriented Markov Decision Processes · AAAI 2016
Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes · IJCAI 2015
Knowledge, reasoning and agents › Knowledge representation and reasoning › reasoning about action and change
domain abstraction		 0.212016
Abstracting Complex Domains Using Modular Object-Oriented Markov Decision Processes · AAAI 2016
Machine learning › Reinforcement learning › markov decision process › structured markov decision process
object-oriented MDPs		 0.212016
Abstracting Complex Domains Using Modular Object-Oriented Markov Decision Processes · AAAI 2016
Machine learning › Reinforcement learning › function approximation › representation learning for reinforcement learning
state abstraction		 0.212016
Abstracting Complex Domains Using Modular Object-Oriented Markov Decision Processes · AAAI 2016
Machine learning › Reinforcement learning › hierarchical reinforcement learning › temporal abstraction
options		 0.212015
Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes · IJCAI 2015
Machine learning › Reinforcement learning › hierarchical reinforcement learning
option discovery		 0.212015
Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes · IJCAI 2015
Machine learning › Reinforcement learning
transfer learning in reinforcement learning		 0.212015
Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes · IJCAI 2015

Methods — techniques the papers use, named apart from their topics

lifted abstract markov decision process · 0.4
YearPublicationVenuePosition
2020 Planning with Abstract Learned Models While Learning Transferable Subtasks
abstract
We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a new formal structure, the lifted abstract Markov decision process (L-AMDP), PALM learns models that are independent and modular. Through our experiments, we show how PALM integrates planning and execution, facilitating a rapid and efficient learning of abstract, hierarchical models. We also demonstrate the increased potential for learned models to be transferred to new and related tasks.
John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, Cynthia Matuszek
 AAAI6
2016 Abstracting Complex Domains Using Modular Object-Oriented Markov Decision Processes
abstract
We present an initial proposal for modular object-oriented MDPs, an extension of OO-MDPs that abstracts complex domains that are partially observable and stochastic with multiple goals. Modes reduce the curse of dimensionality by reducing the number of attributes, objects, and actions into only the features relevant for each goal. These modes may also be used as an abstracted domain to be transferred to other modes or to another domain.
Shawn Squire, Marie desJardins
 AAAI1
2015 Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes
Nicholay Topin, Nicholas Haltmeyer, Shawn Squire, John Winder, Marie desJardins, James MacGlashan
 IJCAI3