# Can Social Agents Efficiently Perform in Automated Negotiation?
## 3 Proposed Social Agent
### Negotiation Strategy  
The behavior of our social agent is determined by a finite state machine that specifies the negotiation behavior to be followed during the negotiation. Each state corresponds to a behavior adopted by the agent in particular circumstances, depending on some factors such as attitude of the opponent, remaining time, or agent's current state. To sum up, our agent's negotiation strategy is composed of five negotiation behaviors. The representation of the finite state machine is shown in Figure 1, and the state transition rules can be explained as follows. In the beginning, our agent aims to get to know its opponent from the bid exchanges. The goal is obtaining knowledge to use in the rest of the negotiation. Therefore, the initial negotiation behavior is _Strictly Exploring_, which is employed until a strict exploration time threshold (\(t_{exp}\)) is surpassed. After the strict exploration phase, the agent may have learned to some extent about its opponent'spreferences, but it may be the case that the learned model is not reliable yet to accurately estimate the bids maximizing the social welfare. In that case, until receiving a certain proportion of unique bids (\(\alpha_{exp}\)), the agent employs a _Exploratory Tit-For-Tat_ behavior. Whenever the agent receives the specified proportion of unique bids, the agent's negotiation behavior turns to be _Nash Optimizer_. In that behavior, the estimated Nash product bid is offered. If the negotiation time surpasses the time threshold (\(t_{sim}\)) and the proportion of unique bids is not received, the _Similarity-Based Tit-For-Tat_ behavior is followed. During this phase, the agent may transit to _Nash Optimizer_ as it receives enough bids. The final behavior _Eager_, occurs only when the negotiation time approaches the deadline (\(t_{eager}\)). The next list briefly describes each of the behaviors:  
* Strictly Exploring: at the start of the negotiation, the agent aims to explore the negotiation space. Therefore, it is not inclined to accept the opponent's bids. The aspiration of the agent is controlled by a special window-based tit-for-tat mechanism that is described in Section 3.2. * Exploratory TFT: After a certain time threshold \(t_{exp}\) in the negotiation, the agent transits to this state. Even though the agent still explores the negotiation space, the agent may accept incoming offers if they are above the current aspiration of the agent (see Section 3.2). * Nash optimizer: As mentioned, our agent's orientation is social. Thus, the agent aims to find a fair deal for both parties, regardless of the opponent's behavior. The strategy of the proposed agent is finding a deal that is as close as possible to the Nash bargaining point. To achieve this, the agent needs to learn an accurate model of the opponent's preferences. Learning an accurate model about the opponent's preferences involves receiving several unique bids from the opponent. When the agent has received a proportion \(\alpha_{exp}\) of the negotiation space, the agent considers the opponent model to be approximately accurate. Then, the agent is ready to change to the _Nash optimizer_ behavior. In this state, the bidding and acceptance strategy try to be as close as possible to the estimated Nash point. * Similarity-based TFT: Although our agent aims to be social and it tries to get as close as possible to the Nash bargaining point, in some situations, the opponent may not reveal many unique bids. Then, in such a case, learning an accurate opponent model becomes hard. Consequently, the agent starts using a similarity-based heuristic when the negotiation is approaching a threshold (i.e., \(t>t_{sim}\)). The aspiration of the agent is again controlled by a special tit-for-tat concession mechanism (see Section 3.2). * Eager: In the last moments of the negotiation (i.e., \(t>t_{eager}\)), the agent tries to close an agreement by proposing back the best offer sent by the opponent or by accepting any offer that improves or equalizes the best offer received so far.