Exploring Monte Carlo Negotiation Search with Nontrivial Agreements


  • Elijah Alden Malaby University of South Florida
  • John Licato University of South Florida




Automated Negotiations, General Game Playing, Monte Carlo Tree Search


The application of automated negotiations to general game playing is a research area with far-reaching implications. Non-zero sum games can be used to model a wide variety of real-world scenarios and automated negotiation provides a framework for more realistically modeling the behavior of agents in these scenarios. A particular recent development in this space is the Monte Carlo Negotiation Search (MCNS) algorithm, which can negotiate to find valuable cooperative strategies for a wide array of games (such as those of the Game Description Language). However, MCNS only proposes agreements corresponding to individual sequences of moves without any higher-level notions of conditional or stateful strategy. Our work attempts to lift this restriction. We present two contributions: extensions to the MCNS algorithm to support more complex agreements and an agreement language for GDL games suitable for use with our algorithm. We also present the results of a preliminary experiment in which we use our algorithm to search for an optimal agreement for the iterated prisoners dilemma. We demonstrate significant improvement of our algorithm over random agreement sampling, although further work is required to more consistently produce optimal agreements.

Author Biography

John Licato, University of South Florida

Assistant Professor, Computer Science and Engineering




How to Cite

Malaby, E. A., & Licato, J. (2021). Exploring Monte Carlo Negotiation Search with Nontrivial Agreements. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128504



Main Track Proceedings