{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T02:17:42Z","timestamp":1783649862134,"version":"3.55.0"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:p>Monte-Carlo Tree Search is a best-first search technique based on simulations to sample the state space of a decision-making problem. In games, positions are evaluated based on estimates obtained from rewards of numerous randomized play-outs. Generally, rewards from play-outs are discrete values representing the outcome of the game (loss, draw, or win), e.g., r&amp;isin;{&amp;minus;1,0,1}, which are backpropagated from expanded leaf nodes to the root node. However, a play-out may provide additional information. In this paper, we introduce new measures for assessing the a posteriori quality of a simulation. We show that altering the rewards of play-outs based on their assessed quality improves results in six distinct two-player games and in the General Game Playing agent CADIAPLAYER. We propose two specific enhancements, the Relative Bonus and Qualitative Bonus. Both are used as control variates, a variance reduction method for statistical simulation. Relative Bonus is based on the number of moves made during a simulation and Qualitative Bonus relies on a domain-dependent assessment of the game's terminal state. We show that the proposed enhancements, both separate and combined, lead to significant performance increases in the domains discussed.<\/jats:p>","DOI":"10.3233\/978-1-61499-419-0-705","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:05Z","timestamp":1740133625000},"source":"Crossref","is-referenced-by-count":7,"title":["Quality-based Rewards for Monte-Carlo Tree Search Simulations"],"prefix":"10.3233","author":[{"family":"Pepels Tom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Tak Mandy J.W.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Lanctot Marc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Winands Mark H. M.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2014"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:33:47Z","timestamp":1740137627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=263&spage=705"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-419-0-705","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2014]]}}}