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Jasper Ding

dblp:218/7426 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—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 · 50% Deep learning architectures and training · 50%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
game playing		 0.312018
Playing SNES Games With NeuroEvolution of Augmenting Topologies · AAAI 2018
Machine learning › Deep learning architectures and training › neural network training
neuroevolution		 0.312018
Playing SNES Games With NeuroEvolution of Augmenting Topologies · AAAI 2018

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

reinforcement learning · 0.3NEAT · 0.3
YearPublicationVenuePosition
2018 Playing SNES Games With NeuroEvolution of Augmenting Topologies
abstract
Teaching a computer to play video games has generally been seen as a reasonable benchmark for developing new AI techniques. In recent years, extensive research has been completed to develop reinforcement learning (RL) algorithms to play various Atari 2600 games, resulting in new applications of algorithms such as Deep Q-Learning or Policy Gradient that outperform humans. However, games from Super Nintendo Entertainment System (SNES) are far more complicated than Atari 2600 games as many of these state-of-the-art algorithms still struggle to perform on this platform. In this paper, we present a new platform to research algorithms on SNES games and investigate NeuroEvolution of Augmenting Topologies (NEAT) as a possible approach to develop algorithms that outperform humans in SNES games.
Son Pham, Keyi Zhang, Tung Phan, Jasper Ding, Christopher L. Dancy
 AAAI4