Monte Carlo Search Algorithm Discovery for Single-Player Games

Maes, Francis; St-Pierre, David Lupien; Ernst, Damien

doi:10.1109/tciaig.2013.2239295

Cited by 7 publications

(4 citation statements)

References 31 publications

(45 reference statements)

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“…First, more advanced tree navigation techniques published in the machine learning literature are investigated [8,14]. Second, instead of considering topological and geometrical operations (such as edge swaps, collapses or node relocation) as post-processing operations, they could be added directly as actions in the sequential decision-making process.…”

Section: Resultsmentioning

confidence: 99%

Sequential decision-making approach for quadrangular mesh generation

Johnen

Ernst

Geuzaine

2014

Engineering with Computers

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Section: Resultsmentioning

confidence: 99%

Sequential decision-making approach for quadrangular mesh generation

Johnen

Ernst

Geuzaine

2014

Engineering with Computers

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“…The PersQ [36] algorithm first used MCTS in itinerary recommendation like single-player game [42]. Before that, most of the researchers applied Monte Carlo tree search for two-player games.…”

Section: Personalized Itinerary Recommendation With Queuing Time Awar...mentioning

confidence: 99%

Efficient itinerary recommendation via personalized POI selection and pruning

et al. 2022

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Personalized itinerary recommendation has garnered wide research interests for their ubiquitous applications. Recommending personalized itineraries is complex because of the large number of points of interest (POI) to consider in order to construct an itinerary based on visitors’ interest and preference, time budget and uncertain queuing time. Previous studies typically aim to plan itineraries that maximize POI popularity, visitors’ interest and minimize queuing time. However, existing solutions may not reflect visitor preferences because when creating itineraries, they prefer to recommend POIs with short prior visiting periods. These recommendations can conflict with real-life scenarios as visitors typically spend less time at POIs that they do not enjoy, thus leading to the inclusion of unsuitable POIs. Moreover, constructing itineraries based on selected POIs is a challenging and time-consuming process. Existing approaches involve searching through a large number of non-optimal, duplicate itineraries that are time-consuming to review and generate. To address these issues, we propose an adaptive Monte Carlo tree search (MCTS)-based reinforcement learning algorithm EffiTourRec using an effective POI selection strategy by giving preference to POIs with long visiting times and short queuing times along with high POI popularity and visitor interest. In addition, to reduce non-optimal and duplicated itineraries generation, we propose an efficient MCTS search pruning technique to explore a smaller, more promising portion of solution space. Experiment results in real theme park datasets show clear advantages of our proposed method over baselines, where our method outperforms the current state-of-the-art by 20.89 to 52.32% in precision, 8.36 to 21.35% in F1-score and 40.00 to 67.64% in execution time.

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“…This is at least a 128-fold speed-up. Following the previous proposed grammar [9], the search components Simulate, Repeat, Lookahead, Select, and…”

Section: The Tree Search Algorithmmentioning

confidence: 99%

Optimal processing for gel electrophoresis images: Applying Monte Carlo Tree Search in GelApp

et al. 2016

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In biomedical research, gel band size estimation in electrophoresis analysis is a routine process. To facilitate and automate this process, numerous software have been released, notably the GelApp mobile app. However, the band detection accuracy is limited due to a band detection algorithm that cannot adapt to the variations in input images. To address this, we used the Monte Carlo Tree Search with Upper Confidence Bound (MCTS-UCB) method to efficiently search for optimal image processing pipelines for the band detection task, thereby improving the segmentation algorithm. Incorporating this into GelApp, we report a significant enhancement of gel band detection accuracy by 55.9 ± 2.0% for protein polyacrylamide gels, and 35.9 ± 2.5% for DNA SYBR green agarose gels. This implementation is a proof-of-concept in demonstrating MCTS-UCB as a strategy to optimize general image segmentation. The improved version of GelApp-GelApp 2.0-is freely available on both Google Play Store (for Android platform), and Apple App Store (for iOS platform).

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Monte Carlo Search Algorithm Discovery for Single-Player Games

Cited by 7 publications

References 31 publications

Sequential decision-making approach for quadrangular mesh generation

Sequential decision-making approach for quadrangular mesh generation

Efficient itinerary recommendation via personalized POI selection and pruning

Optimal processing for gel electrophoresis images: Applying Monte Carlo Tree Search in GelApp

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