English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper makes two proposals for Monte Carlo Softmax Search, which is arecently proposed method that is classified as a selective search like theMonte Carlo Tree Search. The first proposal separately defines thenode-selection and backup policies to allow researchers to freely design anode-selection policy based on their searching strategies and confirms theprincipal variation produced by the Monte Carlo Softmax Search to that producedby a minimax search. The second proposal modifies commonly used learningmethods for positional evaluation functions. In our new proposals, evaluationfunctions are learned by Monte Carlo sampling, which is performed with thebackup policy in the search tree produced by Monte Carlo Softmax Search. Thelearning methods under consideration include supervised learning, reinforcementlearning, regression learning, and search bootstrapping. Our sampling-basedlearning not only uses current positions and principal variations but also theinternal nodes and important variations of a search tree. This step reduces thenumber of games necessary for learning. New learning rules are derived forsampling-based learning based on the Monte Carlo Softmax Search andcombinations of the modified learning methods are also proposed in this paper.

Learning Position Evaluation Functions Used in Monte Carlo Softmax Search