GIB stands for Goren-In-a-Box, and is named after the late Charles Goren, one of the greatest bridge players of all time. GIB was developed by Matthew Ginsberg, an American professor who specializes in artificial intelligence. GIB can not bid yet, nor interpret an auction, but it can declare and defend like a tiger. Many IMP readers are familiar with the training program Bridge Master , a set of 180 hands ranging from simple (level 1) to nearly unsolvable (level 5). Until now, the best score was achieved by the American program Bridge Baron, which brought 33 of the 180 problems (18.3%) to an end successfully. GIB pulverized that score with a mighty 116 correct (64.4%), including 24 of the 36 problems at level five (66.7%). Click HERE for details about the experiment.

References in zbMATH (referenced in 16 articles , 1 standard article )

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  1. Delattre, Sylvain; Fournier, Nicolas: On Monte-Carlo tree search for deterministic games with alternate moves and complete information (2019)
  2. Karwowski, Jan; Mańdziuk, Jacek: A Monte Carlo tree search approach to finding efficient patrolling schemes on graphs (2019)
  3. Powley, Edward J.; Cowling, Peter I.; Whitehouse, Daniel: Information capture and reuse strategies in Monte Carlo Tree Search, with applications to games of hidden information (2014)
  4. Ginsberg, Matthew L.: GIB: imperfect information in a computationally challenging game (2011) ioport
  5. Mandziuk, Jacek: Knowledge-free and learning-based methods in intelligent game playing (2010)
  6. Lu, Hui; Xia, ZhengYou: AWT: Aspiration with timer search algorithm in Siguo (2008)
  7. Oke, S. A.: A literature review on artificial intelligence (2008)
  8. Sturtevant, Nathan R.: An analysis of UCT in multi-player games (2008)
  9. Amit, Asaf; Markovitch, Shaul: Learning to bid in bridge (2006) ioport
  10. Amit, Asaf; Markovitch, Shaul: Learning to bid in bridge (2006)
  11. Panyushev, Dmitri I.; Yakimova, Oksana S.: The index of representations associated with stabilisers (2006)
  12. Ishii, Shin; Fujita, Hajime; Mitsutake, Masaoki; Yamazaki, Tatsuya; Matsuda, Jun; Matsuno, Yoichiro: A reinforcement learning scheme for a partially-observable multi-agent game (2005)
  13. Mossakowski, Krzysztof; Mańdziuk, Jacek: Artificial neural networks for solving double dummy Bridge problems (2004)
  14. Billings, Darse; Davidson, Aaron; Schaeffer, Jonathan; Szafron, Duane: The challenge of poker (2002)
  15. Schaeffer, Jonathan; van den Herik, H. Jaap: Games, computers, and artificial intelligence (2002)
  16. Ginsberg, M. L.: GIB: Imperfect information in a computationally challenging game (2001)