GDL as a unifying domain description language for declarative automated negotiation


Recently, it has been proposed that Game Description Language (GDL) could be used to define negotiation domains. This would open up an entirely new, declarative, approach to Automated Negotiations in which a single algorithm could negotiate over any domain, as long as that domain is expressible in GDL. However, until now, the feasibility of this approach has only been demonstrated on a few toy-world problems. Therefore, in this paper we show that GDL is a truly unifying language that can also be used to define more general and more complex negotiation domains. We demonstrate this by showing that some of the most commonly used test-beds in the Automated Negotiations literature, namely Genius and Colored Trails, can be described in GDL. More specifically, we formally prove that the set of possible agreements of any negotiation domain from Genius (either linear or non-linear) can be modeled as a set of strategies over a deterministic extensive-form game. Furthermore, we show that this game can be effectively described in GDL and we show experimentally that, given only this GDL description, we can explore the agreement space efficiently using entirely generic domain-independent algorithms. In addition, we show that the same holds for negotiation domains in the Colored Trails framework. This means that one could indeed implement a single negotiating agent that is capable of negotiating over a broad class of negotiation domains, including Genius and Colored Trails.

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    We assume that when the agents make an agreement this is considered strictly binding, so they are forced to obey it. The question how the agreements can be enforced is beyond the scope of our work. We simply assume there is some external mechanism that forces the agents to obey their agreements.

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    In the case the negotiator is bounded rational we should say that it would never accept any proposal it expects to yield utility less than its reservation value.

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    In the literature the utility functions are usually defined as weighted sums. However, we can omit the weights without loss of generality, because they can be absorbed into the \(d_{i,j}\).

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    Note that strictly speaking it is not necessary to include T in this tuple, because T is already implied in the definition of the graph \(\langle V, E\rangle\). However, we prefer to include it, for clarity.

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    Note that we use the notation m(vw) as a shorthand for m((vw)).

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    One might be tempted to use the term subgame instead of restriction, but the term subgame already has a different meaning in Game Theory. In [33] we used the concept of a strategy, instead of a restriction. Although strategies and restrictions are defined differently, they represent essentially the same thing.

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    This is not exactly the same as a noop-move that is commonly used in GDL to model turn-taking games. A noop-move represents the case that the player does not move because it is not their turn, while the waive move means that it is that player’s turn, but the player chooses to do nothing.

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    Of course, there may exist more sophisticated Genetic Algorithms that are able to avoid this problem, but to the best of our knowledge no one has ever tried to apply those to automated negotiations.


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This work was funded by the E.U. Horizon 2020 Research and Innovation Programme, Grant Agreement No. 769142, Project LOGISTAR.

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de Jonge, D., Zhang, D. GDL as a unifying domain description language for declarative automated negotiation. Auton Agent Multi-Agent Syst 35, 13 (2021).

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  • Automated negotiations
  • Game description language
  • Non-zero-sum games
  • Extensive-form games
  • General game playing
  • Monte Carlo tree search