# Can Social Agents Efficiently Perform in Automated Negotiation?
## 3 Proposed Social Agent
### Opponent Modeling
#### 3.5.2 Issue Weight Estimation
* Negotiation time: Although it is not related to the fairness of the agreement found by negotiating agents, the negotiation time helps to see the performance of the different strategies from a different perspective. The sooner agreements are closed, the more time that the spare time and computational power can be allocated to other relevant tasks. Therefore, other aspects being equal, one should prefer negotiation agents that close deals more rapidly. The aforementioned metrics are employed to analyze the empirical performance of our social agent and other agents employed in the experiments. #### 4.1.2 Negotiation Domains

In these experiments, we considered four different negotiation domains with a medium or large number of possible negotiation outcomes to reduce the odds of the Nash point or agreements close by mere chance. Next, we briefly describe the negotiation domains chosen for the experiments:

* Itex vs. Cypress (ANAC 2010, [34]): This is a classic buyer-seller setting where a bike manufacturing company negotiates on the sale of a commodity with a bike retail shop. There are a total of 180 possible outcomes. * England vs. Zimbabwe (ANAC 2010, [34]): This is a negotiation domain consisting of the national negotiations between England and Zimbabwe on the application of the World's Health Organization Framework on Tobacco Control. The negotiation domain consists of 576 possible negotiation outcomes and 5 negotiation issues. * Grocery domain (ANAC 2011, [35]): This domain involves agents negotiating on the type of groceries to purchase from a supermarket. There are a total of 1600 possible outcomes distributed across 5 negotiation issues. * Amsterdam Trip (ANAC 2011, [35]): This is a negotiation domain where two parties of friends negotiate on the details of a trip to Amsterdam. The negotiation domain contains 3024 possible outcomes distributed across 6 negotiation issues. * Party domain: This is a negotiation party where two hosts negotiate on how to organize a party for their friends. The negotiation scenario contains 3072 possible negotiation outcomes and 6 negotiation issues. * Camera domain (ANAC 2012, [36]): The camera domain contains preference profiles that describe the purchase of a camera and its accessories for a specific budget. More specifically, the negotiation domain contains 3600 prospective outcomes distributed across 6 negotiation issues. #### 4.1.3 Agents

Another critical decision to make for the experimental setting is deciding on the opponent agents to negotiate with. Some factors influenced our decision in this matter. First, our proposed agent is an agent that models the opponent from the data leaked in the current negotiation process, and we assume that no other data from the opponent is available. This is the situation faced in case of a cold start in an agent society, in case that the preferences of agents change from negotiation to negotiation, or in the case that negotiation partners rarely interact more than once. Therefore, negotiating agents that learn from multiple negotiations are excluded from this experimental setting, as introducing these agents would result in an unfair comparison. Second, we want to evaluate the performance of our agent in an agent society that shows a range of behaviors and bidding strategies, as it would probably happen in a real agent society. For that reason, we employ the following state-of-the-art agents to represent a realistic agent society:

* AgentK (K) [37]: This is the winning agent of the 2010 ANAC negotiation competition. It is an agent for which the concession speed is regulated by the average utility of all received bids and its standard deviation. In terms of offer proposal, it selects any offer from above the current aspiration. Therefore, it does not model the opponent's preferences on offers. * [PERSON] (IH2011) [38]: A negotiating agent that uses Gaussian processes to predict the future concession of its opponent and then adjust its concession rate accordingly to get the most from the negotiation.