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Connor Berson

dblp:245/3281 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

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
1 paper
Planning, search and constraint satisfaction · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › tree search
depth-first search
0.412019
Efficient Solving of Birds of a Feather Puzzles · AAAI 2019
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
puzzle solving
0.112019
Efficient Solving of Birds of a Feather Puzzles · AAAI 2019
YearPublicationVenuePosition
2019 Efficient Solving of Birds of a Feather Puzzles
abstract
In this article, we describe the lessons learned in creating an efficient solver for the solitaire game Birds of a Feather. We introduce a new variant of depth-first search that we call best-n depth-first search that achieved a 99.56% reduction in search time over 100,000 puzzle seeds. We evaluate a number of potential node-ordering search features and pruning tests, perform an analysis of solvability prediction with such search features, and consider possible future research directions suggested by the most computationally expensive puzzle seeds encountered in our testing.
Todd W. Neller, Connor Berson, Jivan Kharel, Ryan Smolik
AAAI2