# Can Social Agents Efficiently Perform in Automated Negotiation?
## 3 Proposed Social Agent
### Opponent Modeling
#### 3.5.2 Issue Weight Estimation
The goal of this agent is that of optimizing one's utility while also trying to get more utility than the opponent. Bids are only selected from a small range around the target utility. This aggressive stance usually results in the agent repeating the same offers. This agent placed third in the ANAC 2011 competition. * [PERSON] (GAH) [39]: This agent finished second in the ANAC 2011 competition. [PERSON] employs an adaptive concession strategy that attempts to detect if agents are susceptible to being pressured or if they are stubborn. For that, [PERSON] limits most of the outcomes sent to outcomes that report a low utility value for the opponent. * Hardheaded (HH) agent [25]: The winning agent of the 2011 ANAC competition. This agent follows a time-based bidding strategy that consists of not conceding until almost the end of the negotiation. While not conceding, the agent performs opponent modeling on the opponent's preferences by using a frequency modeling mechanism. Then, in the last states of the negotiation, the agent attempts to find an agreement by using the trained model on the opponent's preferences and finding outcomes that have the same target utility for one's own but maximize the preferences of the opponent. * IAmHaggler2012 (IH2012) [36]: The 2012 version of IAmHaggler ranked as the most social agent in the ANAC 2012 competition (i.e., maximization of the sum of the opponent's and one's utility). This agent was employed to compare the performance of our social agent against some state-of-the-art social agents. TheNegotiatorReloaded (TNR) [40]: This agent is the best performing agent in non-discounted domains for the ANAC 2012 agent competition. The agent divides the negotiation into non-sliding windows, similarly to our approach. For each window, the agent estimates the type of agent behavior that it is facing and adjusts its concession rate accordingly. The agent employs Bayesian learning for modeling the opponents' preferences and having an updated estimation of the Kalai point. * CUHK Agent (CUHK) [41]: This agent won the ANAC 2012 agent competition for discounted domains. The agent adopts an adaptive concession strategy that sets a minimum target utility depending on both the behavior of the opponent and the characteristics of the domain (i.e., discount factor). In general, the agent will attempt to exploit the opponent as much as possible by not conceding. Compromises are only taken at the end of the negotiation after a time threshold that is set dynamically according to the opponent's behavior. In this case, the agent attempts to model the opponent preferences by attempting to estimate a rank for proposed outcomes. From the agents selected, one can observe that we have both agents that carry out opponent modeling (i.e., [PERSON], Hardheaded, TheNegotiatorReloaded, CUHK Agent, and IAmHaggler2012), agents that show an adaptive concession behavior (e.g., AgentK, [PERSON], TheNegotiatorReloaded, CUHK Agent, [PERSON], and IAmHaggler2012), and agents that consider the opponent's preferences when constructing bids (e.g., Hardheaded, TheNegotiatorReloaded, and IAmHaggler2012). They are agents that performed among the best in the International Automated Negotiating Agents Competition (ANAC) editions of 2010, 2011, and 2012. It should be noted that agents from the 2013 competition onward were discarded as they specialize in different problems to the ones covered in this article (e.g., learning over multiple negotiations, non-linear utility functions, multi-lateral negotiations, etc.). Thus, we argue that a realistic agent society would be formed by top-performing agents as most of the ones employed in this setting. #### 4.1.4 Social Agent Configuration

As the reader may have realized, our agent's behavior is controlled by many hyperparameters. After an initial exploration of our agent, we found a set of parameter values that generally performed well in practice.