Exploring Monte Carlo Negotiation Search with Nontrivial Agreements

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.

Optimal Auction Based Automated Negotiation in Realistic Decentralised Market Environments

Automated negotiations based on learning models have been widely applied in different domains of negotiation. Specifically, for resource allocation in decentralised open market environments with multiple vendors and multiple buyers. In such open market environments, there exists dynamically changing supply and demand of resources, with dynamic arrival of buyers in the market. Besides, each buyer has their own set of constraints, such as budget constraints, time constraints, etc. In this context, efficient negotiation policies should be capable of maintaining the equilibrium between the utilities of both the vendors and the buyers. In this research, we aim to design a mechanism for an optimal auction paradigm, considering the existence of interdependent undisclosed preferences of both, buyers and vendors. Therefore, learning-based negotiation models are immensely appropriate for such open market environments; wherein, self-interested autonomous vendors and buyers cooperate/compete to maximize their utilities based on their undisclosed preferences. Toward this end, we present our current proposal, the two-stage learning-based resource allocation mechanism, wherein utilities of vendors and buyers are optimised at each stage. We are aiming to compare our proposed learning-based resource allocation mechanism with two state-of-the-art bidding-based resource allocation mechanism, which are based on, fixed bidding policy (Samimi, Teimouri, and Mukhtar 2016) and demand-based bidding policy (Kong, Zhang, and Ye 2015). The comparison is to be done based on the overall performance of the open market environment and also based on the individual performances of vendors and buyers.