# Can Social Agents Efficiently Perform in Automated Negotiation?
## 3 Proposed Social Agent
### Opponent Modeling
#### 3.5.2 Issue Weight Estimation
The Nice Tit-For-Tat [29] strategy was proposed in one of the Automated Negotiating Agents Competitions to reciprocate the opponents' behavior. Similar to our proposal, the agent is equipped with an opponent model, one based on Bayesian learning, that helps to estimate the opponent's preferences on bids. This opponent model guides the concession \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Social** & **K** & **GAH** & **HH** & **IH2011** & **TNR** & **CUHK** & **IH2012** \\ \hline
0.72/0.72 & 0/0 & 0/0 & 0.28/0.28 & 0/0 & 0/0 & 0/0 & 0/0 \\ \hline
0.77/0.77 & 0/0 & 0.05/0.05 & 0.19/0.19 & 0/0 & 0/0 & 0/0 & 0/0 \\ \hline
0.50/0.50 & 0/0 & 0.47/0.47 & 0/0.0 & 0/0 & 0/0 & 0.04/0.04 & 0/0 \\ \hline
0.92/0 & 0/0.48 & 0.06/0.32 & 0/0 & 0/0 & 0/0.21 & 0.02/0 & 0/0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mixed Nash equilibrium for the dynamic agent society. Each row represents a different mixed equilibrium. In each cell, the first number represents the ratio of use of the strategy for the first player, while the second represents the ratio of use of the same strategy for the second player. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Social** & **K** & **GAH** & **HH** & **IH2011** & **TNR** & **CUHK** & **IH2012** \\ \hline
**Social** & 0.79/0.79 & 0.81/0.81 & 0.79/0.81 & 0.80/0.82 & 0.80/0.82 & **0.77/0.82** & 0.88/0.67 & 0.81/0.80 \\ \hline
**K** & 0.81/0.81 & 0.81/0.81 & 0.67/0.89 & 0.64/0.91 & 0.80/0.80 & 0.62/0.91 & 0.81/0.77 & 0.86/0.75 \\ \hline
**GAH** & 0.81/0.79 & 0.89/0.67 & 0.80/0.80 & 0.73/0.61 & 0.94/0.60 & 0.59/0.58 & 0.55/0.94 & 0.90/0.66 \\ \hline
**HH** & 0.82/0.80 & 0.91/0.64 & 0.61/0.73 & 0.72/0.72 & 0.92/0.63 & 0.60/0.64 & 0.56/0.79 & 0.91/0.65 \\ \hline
**IH2011** & 0.82/0.80 & 0.80/0.80 & 0.60/0.94 & 0.63/0.92 & 0.81/0.81 & 0.55/0.95 & 0.50/0.97 & 0.84/0.78 \\ \hline
**TNR** & **0.82/0.77** & 0.91/0.62 & 0.58/0.59 & 0.64/0.60 & 0.95/0.55 & 0.59/0.59 & 0.59/0.64 & 0.91/0.64 \\ \hline
**CUHK** & 0.67/0.88 & 0.77/0.81 & 0.94/0.55 & 0.79/0.56 & 0.97/0.50 & 0.64/0.59 & 0.73/0.73 & 0.82/0.75 \\ \hline
**IH2012** & 0.80/0.81 & 0.75/0.86 & 0.66/0.90 & 0.65/0.91 & 0.78/0.84 & 0.64/0.91 & 0.75/0.82 & 0.81/0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Payoff matrix for a bilateral negotiation in an agent society formed by the studied agents. Bold represents the pure equilibrium of the game strategy carried out by the tit-for-tat agent. When the opponent carries out a concession, the agent reciprocates some proportion of the opponent's concession and selects the bid that is the most likely to be accepted by the opponent according to the opponent model. Our agent also employs an opponent model to drive bidding, but it is aware when the model may not be trusted due to the lack of information about the opponent. In addition to this, concessions are not detected solely based on a single pair of bids, but a window of bids is employed to ensure that the concession is a byproduct of a general and genuine change by the opponent, and it is not a byproduct of mere chance. [PERSON] et al. [52] proposed an interesting approach that combines machine learning and statistics for appropriately selecting a negotiation strategy. Two mechanisms are proposed: one based on supervised learning that selects a negotiation depending on the negotiation domain characteristics and another based on multi-armed bandits that selects the negotiation strategy during the negotiation. The results showed that the agent was capable of over-performing agents for the previous negotiation competitions. Our agent also adapts its behavior during the negotiation. Even though the adaptive mechanism is not as sophisticated, it is engineered based on the prospective problems that may arise during the negotiation. On top of that, the main contribution of the article is not the adaptation mechanism itself, but the proposal of a social strategy that is capable of efficiently performing in an agent society.