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Concurrent local negotiations with a global utility function: a greedy approach

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Abstract

Automated Negotiation is a growing area of research in recent years as it provides a mechanism for intelligent agents representing people and institutions to coordinate their behavior in a complex environment under rational selfish assumptions. Most research in this area assumes either a single negotiation thread with a well-defined utility function for each agent involved or a set of concurrent negotiations with an ordering of outcomes in each local negotiation. In this paper, we consider an agent engaged in a set of concurrent negotiations with a utility function defined only for the complete set of agreements in all of them and no locally defined ordering of outcomes in any negotiation. The paper presents an algorithm that allows such agent to maximize its expected global utility by orchestrating its behavior in all negotiation threads. The performance of the proposed method is analyzed theoretically and empirically using simulation in the context of a trading market.

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Notes

  1. Extensions to multilateral negotiations on every thread and general finite range utility functions are straightforward.

  2. WilliamsGP is a faithful impelementation of the original algorithm. WilliamsAM is intended to test whether having a precompiled acceptance model improves or worsens the performance of the algorithm in comparisons with the proposed method.

  3. Agents participating in SCML have to solve several problems other than concurrent negotiation (e.g. production scheduling, long term business planning, ufun definition, etc). For this reason none of these agents can be used directly here. SCML2019 agents in this study are extracted from the built-in Greedy Factory Manager agent which was inherited by SCML participating agents keeping only the concurrent negotiation strategy described in this section.

  4. Equation 5 shows that concurrent negotiation is equivalent to a decoupled set of single-thread negotiations with dynamically varying utility functions which sheds light on why SCML2019 algorithms work.

  5. Using Caduceus [15], ParsCat and YXAgent agents (winners of ANAC 2016) instead of boulware did not improve the performance of SCML2019 variants. Boulware was the strategy suggested in [29] and is commonly employed by concurrent negotiation algorithms [32, 42].

  6. To avoid unnecessary cumbersome notation, we assume that one unit of input (tomato) is used to generate one unit of output (sandwich)

  7. The main reason for having a small outcome space here (50) is to reduce the computational cost of running many simulations. Nevertheless, these kinds of concurrent negotiations with small outcomes spaces are common in real business scenarios. One example is daily delivery schedule negotiation that is usually conducted in SCM situations under the constraint of a yearly contract dictating prices. In the air-cargo scenario discussed earlier, the same kind of rescheduling negotiations take place between forwarders and carriers.

  8. The \(10^{12}\) difference in the p value here shows the higher power of paired t test comared with the Kolmogorov-Smirnov test as it compares algorithms on the same scenario instead of having to compare the sets of scores that is subject to other sources of variation (e.g. variation scenario paramters).

  9. Benferroni’s multiple comparisons correction is employed to avoid reporting statistically-significant results that are just a consequence of running multiple t tests. In the specific case of our experiments, all statistically significant results had low enough p value that Benferroni’s correction did not make any of them statistically insignificant.

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Acknowledgements

This work was done at the NEC-AIST AI Collaborative Research Laboratory.

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Authors and Affiliations

  1. NEC CORPORATION, Tokyo, Japan

    Yasser Mohammad

  2. National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

    Yasser Mohammad

  3. Assiut University, Asyut, Egypt

    Yasser Mohammad

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Mohammad, Y. Concurrent local negotiations with a global utility function: a greedy approach. Auton Agent Multi-Agent Syst 35, 28 (2021). https://doi.org/10.1007/s10458-021-09512-y

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