diff --git a/README.md b/README.md
index e655819..10b4ddb 100644
--- a/README.md
+++ b/README.md
@@ -237,7 +237,9 @@ If you aren't able to access any paper on this list, please [try using Sci-Hub](
 - [An Intelligent Artificial Player for the Game of Risk](http://www.ke.tu-darmstadt.de/lehre/archiv/ss04/oberseminar/folien/Wolf_Michael-Slides.pdf) (presentation)
 - [RISKy Business: An In-Depth Look at the Game RISK](https://scholar.rose-hulman.edu/rhumj/vol3/iss2/3) (journalArticle)
 - [RISK Board Game ‐ Battle Outcome Analysis](http://www.c4i.gr/xgeorgio/docs/RISK-board-game%20_rev-3.pdf) (journalArticle)
-- [A multi-agent system for playing the board game risk]() (book)
+- [A multi-agent system for playing the board game risk](http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3781) (thesis)
+- [Monte Carlo Tree Search for Risk](https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01.pdf) (conferencePaper)
+- [Wargaming with Monte-Carlo Tree Search](https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-SAS-OCS-ORA-2020/MP-SAS-OCS-ORA-2020-WCM-01P.pdf) (presentation)
 
 # Santorini
 - [A Mathematical Analysis of the Game of Santorini](https://openworks.wooster.edu/independentstudy/8917/) (thesis)
diff --git a/boardgame-research.rdf b/boardgame-research.rdf
index 3558a6f..ecaf251 100644
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         <dc:title>An Intelligent Artificial Player for the Game of Risk</dc:title>
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         <dc:title>A multi-agent system for playing the board game risk</dc:title>
+        <dcterms:abstract>Risk is a game in which traditional Artificial-Intelligence methods such as for example iterative deepening and Alpha-Beta pruning can not successfully be applied due to the size of the search space. Distributed problem solving in the form of a multi-agent system might be the solution. This needs to be tested before it is possible to tell if a multi-agent system will be successful at playing Risk or not. In this thesis the development of a multi-agent system that plays Risk is explained. The system places an agent in every country on the board and uses a central agent for organizing communication. An auction mechanism is used for negotiation. The experiments show that a multi-agent solution indeed is a prosperous approach when developing a computer based player for the board game Risk.</dcterms:abstract>
         <dc:date>2005</dc:date>
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+        <dc:title>Monte Carlo Tree Search for Risk</dc:title>
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         <dc:title>Santorini</dc:title>