ArticleScience progress
A decision-making framework using MCTS as a hierarchical task network and deep learning connector.
Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, purely deep learning-based agents struggle to make optimal decisions within a short timeframe in problems with a vast decision-making space. Human planning knowledge is required to assist agents in making better decisions. This manuscript proposes a novel knowledge-guided and data-driven decision-making framework, utilizing hierarchical task network as the carrier of knowledge, deep learning as the trainer for data, and the Monte Carlo Tree Search as the connector between hierarchical task network and deep learning. The experiments on the MiniRTS environment validated that the proposed framework in this manuscript can replace humans in collecting high-quality data, and it can train neural networks that perform equally well as the compared network even with only 20% of the available data, which provide a new direction for future research.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.