GAMUT

Welcome to GAMUT -- GAMUT is a suite of game generators designated for testing game-theoretic algorithms. With GAMUT, instances of games from thirty-five base game classes can be easily generated. Using different parameterization options, it is possible to randomize over countless distributions of games, resulting in a comprehensive test bed for any algorithm requiring a normal-form game as input. GAMUT is not to be confused with Gambit, a library of game theory software and tools for the construction and analysis of finite extensive and normal form games. GAMUT is made to be used in collaboration with Gambit.


References in zbMATH (referenced in 17 articles )

Showing results 1 to 17 of 17.
Sorted by year (citations)

  1. Han, Dongge; Harrenstein, Paul; Nugent, Steven; Philpott, Jonathan; Wooldridge, Michael: Behavioural strategies in weighted Boolean games (2021)
  2. Basilico, Nicola; Coniglio, Stefano; Gatti, Nicola; Marchesi, Alberto: Bilevel programming methods for computing single-leader-multi-follower equilibria in normal-form and polymatrix games (2020)
  3. Coniglio, Stefano; Gatti, Nicola; Marchesi, Alberto: Computing a pessimistic Stackelberg equilibrium with multiple followers: the mixed-pure case (2020)
  4. Crandall, Jacob W.: When autonomous agents model other agents: an appeal for altered judgment coupled with mouths, ears, and a little more tape (2020)
  5. Enkhbat, Rentsen; Batbileg, S.; Tungalag, N.; Anikin, A.; Gornov, A.: A global optimization approach to nonzero sum six-person game (2020)
  6. Batbileg, S.; Tungalag, N.; Anikin, A.; Gornov, A.; Finkelstein, E.: A global optimization algorithm for solving a four-person game (2019)
  7. Elgers, Niels; Dang, Nguyen; De Causmaecker, Patrick: A metaheuristic approach to compute pure Nash equilibria (2019)
  8. Enkhbat, Rentsen; Batbileg, Sukhee; Anikin, Anton; Tungalag, Natsagdorj; Gornov, Alexander: A note on four-players triple game (2019)
  9. Fearnley, John; Gairing, Martin; Goldberg, Paul W.; Savani, Rahul: Learning equilibria of games via payoff queries (2015)
  10. Boryczka, Urszula; Juszczuk, Przemyslaw: A new evolutionary approach for computing Nash equilibria in bimatrix games with known support (2012) ioport
  11. Jiang, Albert Xin; Leyton-Brown, Kevin; Bhat, Navin A. R.: Action-graph games (2011)
  12. Roughgarden, Tim: Computing equilibria: a computational complexity perspective (2010)
  13. Porter, Ryan; Nudelman, Eugene; Shoham, Yoav: Simple search methods for finding a Nash equilibrium (2008)
  14. Guerin, Frank: Applying game theory mechanisms in open agent systems with complete information. (2007) ioport
  15. Powers, Rob; Shoham, Yoav; Vu, Thuc: A general criterion and an algorithmic framework for learning in multi-agent systems (2007)
  16. Shoham, Yoav; Powers, Rob; Grenager, Trond: If multi-agent learning is the answer, what is the question? (2007)
  17. Tuyls, Karl; Hoen, Pieter Jan’t; Vanschoenwinkel, Bram: An evolutionary dynamical analysis of multi-agent learning in iterated games. (2006) ioport