William Maga

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1ranked-venue papers

0as first author

0since 2021 · last 2016

—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 · 60% Probabilistic and Bayesian machine learning · 20% Reinforcement learning · 20%

Topics — the 5 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction game playing	0.2	1	2016	BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares · AAAI 2016
Machine learning › Reinforcement learning › multi-armed bandit greedy algorithm	0.2	1	2016	BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares · AAAI 2016
Knowledge, reasoning and agents › Planning, search and constraint satisfaction heuristic search	0.2	1	2016	BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares · AAAI 2016
Machine learning › Probabilistic and Bayesian machine learning monte carlo methods	0.2	1	2016	BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares · AAAI 2016
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › game tree search monte carlo tree search	0.2	1	2016	BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares · AAAI 2016

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

monte carlo simulation · 0.2iterative improvement · 0.2heuristic learning · 0.2

Year	Publication	Venue	Position
2016	BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares abstract We investigated Parameterized Poker Squares to approximate an optimal game playing agent. We organized our inquiry along three dimensions: partial hand representation, search algorithms, and partial hand utility learning. For each dimension we implemented and evaluated several designs, among which we selected the best strategies to use for BeeMo, our final product. BeeMo uses a parallel flat Monte-Carlo search. The search is guided by a heuristic based on hand patterns utilities, which are learned through an iterative improvement method involving Monte-Carlo simulations and optimized greedy search. Karo Castro-Wunsch, William Maga, Calin Anton	AAAI	2