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Learning from the Metaheuristics: Protocols for Automated Negotiations

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Abstract

Nowadays, enterprises are more and more interconnected such that operational planning has to consider the different interests of the involved organizations. This may be a challenging and complex task as it is subject to strategic interactions and incomplete information. Automated negotiation by software agents is a powerful tool which can handle these issues and facilitate intercompany planning. Nevertheless, sophisticated negotiation protocols that govern the rules of the negotiation are needed. In this study, we present and evaluate two configurable protocols for multi-issue negotiations, which are inspired by general heuristic optimization algorithms for centralized problems, so-called metaheuristics. The protocols consist of several policy building blocks; these are evaluated with regard to their impact on the negotiation outcome. The evaluation shows that both protocols can efficiently achieve beneficial solutions—even for complex, nonlinear contract spaces—given the parameterization and the configuration of building blocks are chosen appropriately. Furthermore, we elaborate on requirements for appropriate protocol design and find that both protocols adequately comply with the requirements.

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Notes

  1. There is also a multi-bilateral case with more than two agents, but only bilateral connections (one-to-many).

  2. With the origin as disagreement point.

  3. For instance, the other companies can adjust their advertising activity by using information deduced from rather unimportant operations management actions, i.e., the information can be linked to a more important context outside of the negotiation.

  4. See Sect. 2.2.2 for definitions of \(c^p,c^n,\) and \(c^k\); furthermore, \(c_j^{best} = \arg \max _c U_j(c)\) (claim point) and \(c^p = \arg \min _{c^p \in \mathcal {P}} EuclideanDistance(c,c^p)\) (closest Pareto point).

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  1. Helmut Schmidt University Hamburg, Holstenhofweg 85, 22043,  Hamburg, Germany

    Fabian Lang & Andreas Fink

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  1. Fabian Lang
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Correspondence to Fabian Lang.

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Lang, F., Fink, A. Learning from the Metaheuristics: Protocols for Automated Negotiations. Group Decis Negot 24, 299–332 (2015). https://doi.org/10.1007/s10726-014-9390-x

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