The domains of the negotiation can vary significantly. It is possible that a domain is very cooperative, where both agents can receive a high utility; the opposite is also possible, where the domain is very competitive and the agents cannot both get a high utility. In the same manner, the agents can have different strategies leading to a complicated problem with no obvious solution.

This research seeks to represent the differences in negotiation domains to improve a machine learning based agent to help the agent generalize these domains. To achieve this several ways of representing the domain have been explored.
First is the shared domain information. With this representation, the agent uses information about the amount of issues, values and possible bids there are. Second is the private domain information, in this representation, the agent uses different calculations to get a view of how favorable the domain is in terms of utility. Last is the derived information, this is the representation where the agent learns about the domain by interaction with the environment or the opposing agent.

From the experiments, a conclusion could be made that a part of these representations had a positive impact on the final utility of the agent. The shared domain information had a considerable improvement over the base agent with the features having a non-negligible impact on the negotiation. The derived information also had a considerable impact on the final outcome.