Exploring Monte Carlo Negotiation Search with Nontrivial Agreements

Abstract

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.

A Systematic Mapping Study on Automated Negotiation for Autonomous Intelligent Agents

“…Indeed, when negotiating for a price, the agents only need to encode the preferred and reserved price values that the agent wants to bid on and receive to maximize its reward. However, negotiating for resources of other kinds would require encoding different types of data, such as dialogues [26], rules [66], texts, images, or even data from sensors. One example is the work presented in PS36, in which the autonomous agent grants access to user data on her smartphone to an online service in exchange for a monetary reward.…”