Mechanism design for automated negotiation, and its application to task oriented domains

As distributed systems of computers play an increasingly important role in society, it will be necessary to consider ways in which these machines can be made to interact effectively. Especially when the interacting machines have been independently designed, it is essential that the interaction environment be conducive to the aims of their designers. These designers might, for example, wish their machines to behave efficiently, and with a minimum of overhead required by the coordination mechanism itself. The rules of interaction should satisfy these needs, and others. Formal tools and analysis can help in the appropriate design of these rules. We here consider how concepts from game theory can provide standards to be used in the design of appropriate negotiation and interaction environments. This design is highly sensitive to the domain in which the interaction is taking place. Different interaction mechanisms are suitable for different domains, if attributes like efficiency and stability are to be maintained. We present a general theory that captures the relationship between certain domains and negotiation mechanisms. The analysis makes it possible to categorize precisely the kinds of domains in which agents find themselves, and to use the category to choose appropriate negotiation mechanisms. The theory presented here both generalizes previous results, and allows agent designers to characterize new domains accurately. The analysis thus serves as a critical step in using the theory of negotiation in real-world applications. We show that in certain task oriented domains, there exist distributed consensus mechanisms with simple and stable strategies that lead to efficient outcomes, even when agents have incomplete information about their environment. We also present additional novel results, in particular that in concave domains using all-or-nothing deals, no lying by an agent can be beneficial, and that in subadditive domains, there often exist beneficial decoy lies that do not require full information regarding the other agent's goals.