Targeted Data Acquisition for Evolving Negotiation Agents

Consider a standard non-cooperative negotiation game (Deming et al., 1944; Successful negotiators must learn how to balance Nash, 1950; 1951) as shown in Figure 1 where two agents - optimizing for self-interest and cooperation. Yet Alice and Bob - are trying to agree on an allocation of shared current artificial negotiation agents often heavily resources. Both have high utility associated with the hats depend on the quality of the static datasets they and balls, though Alice also cares about books. Effectively were trained on, limiting their capacity to fashion employing negotiation is crucial, and is the only way to an adaptive response balancing self-interest and reach an equitable outcome - dividing the hats and balls cooperation. For this reason, we find that these evenly, while giving Alice the book. Even where negotiating agents can achieve either high utility or cooperation, agents have incentives that make it challenging for them to but not both. To address this, we introduce cooperate, it would be difficult to imagine that negotiation a targeted data acquisition framework where we could be useful to agents over time -- let alone society -- guide the exploration of a reinforcement learning if agents were incapable of cooperating to achieve equitable agent using annotations from an expert oracle.