Calculating Ultrastrong and Extended Solutions for Nine Men’s Morris, Morabaraba, and Lasker Morris | IEEE Journals & Magazine | IEEE Xplore

Calculating Ultrastrong and Extended Solutions for Nine Men’s Morris, Morabaraba, and Lasker Morris


Abstract:

The strong solutions of Nine Men's Morris and its variant, Lasker Morris, are well-known results (the starting positions are draws). We reexamined both of these games, an...Show More

Abstract:

The strong solutions of Nine Men's Morris and its variant, Lasker Morris, are well-known results (the starting positions are draws). We reexamined both of these games, and calculated extended strong solutions for them. By this, we mean the game-theoretic values of all possible game states that could be reached from certain starting positions where the number of stones to be placed by the players is different from the standard rules. These were also calculated for a previously unsolved third variant, Morabaraba, with interesting results: most of the starting positions where the players can place an equal number of stones (including the standard starting position) are wins for the first player (as opposed to the above games, where these are usually draws). We also developed a multivalued retrograde analysis, and used it as a basis for an algorithm for solving these games ultra-strongly. This means that when our program is playing against a fallible opponent, it has a greater chance of achieving a better result than the game-theoretic value, compared to randomly selecting between “just strongly” optimal moves. Previous attempts on ultrastrong solutions used local heuristics or learning during games, but we incorporated our algorithm into the retrograde analysis.
Page(s): 256 - 267
Date of Publication: 06 April 2015

ISSN Information:

Author image of Gábor E. Gévay
Eötvös Loránd University
Gábor E. Gévay received the B.Sc. degree in computer science from Eötvös Loránd University, Budapest, Hungary, in 2012, where he is currently working toward the M.Sc. degree.
He is currently working at Ericsson on a distributed complex event processing system, and contributing to Apache Flink. His main research interests include distributed stream processing, artificial intelligence of board games, and computational number...Show More
Gábor E. Gévay received the B.Sc. degree in computer science from Eötvös Loránd University, Budapest, Hungary, in 2012, where he is currently working toward the M.Sc. degree.
He is currently working at Ericsson on a distributed complex event processing system, and contributing to Apache Flink. His main research interests include distributed stream processing, artificial intelligence of board games, and computational number...View more
Author image of Gábor Danner
University of Szeged
Gábor Danner received the M.Sc. degree in computer science from the University of Szeged, Szeged, Hungary, in 2014, where he is currently working toward the Ph.D. degree.
His main research interests include fully distributed machine learning and games.
Gábor Danner received the M.Sc. degree in computer science from the University of Szeged, Szeged, Hungary, in 2014, where he is currently working toward the Ph.D. degree.
His main research interests include fully distributed machine learning and games.View more

Author image of Gábor E. Gévay
Eötvös Loránd University
Gábor E. Gévay received the B.Sc. degree in computer science from Eötvös Loránd University, Budapest, Hungary, in 2012, where he is currently working toward the M.Sc. degree.
He is currently working at Ericsson on a distributed complex event processing system, and contributing to Apache Flink. His main research interests include distributed stream processing, artificial intelligence of board games, and computational number theory.
Gábor E. Gévay received the B.Sc. degree in computer science from Eötvös Loránd University, Budapest, Hungary, in 2012, where he is currently working toward the M.Sc. degree.
He is currently working at Ericsson on a distributed complex event processing system, and contributing to Apache Flink. His main research interests include distributed stream processing, artificial intelligence of board games, and computational number theory.View more
Author image of Gábor Danner
University of Szeged
Gábor Danner received the M.Sc. degree in computer science from the University of Szeged, Szeged, Hungary, in 2014, where he is currently working toward the Ph.D. degree.
His main research interests include fully distributed machine learning and games.
Gábor Danner received the M.Sc. degree in computer science from the University of Szeged, Szeged, Hungary, in 2014, where he is currently working toward the Ph.D. degree.
His main research interests include fully distributed machine learning and games.View more
Contact IEEE to Subscribe

References

References is not available for this document.