# Can Social Agents Efficiently Perform in Automated Negotiation?
## 1 Introduction
In contrast, if the opponent's strategy is designed in a way that it does not respond to a nice move, it may not be beneficial to sustain the negotiation or to make any further concessions. In those cases, it may be good to signal when we are not pleased with an opponent's attitude during the negotiation. For example, given a selfish opponent, a counter selfish move may help the opponent notice that we are not inclined to concede against its selfish attitude [26]. Therefore, concessions should be carried out attending to one's own and the opponent's moves. Second, achieving a social agreement, one that is appealing for both parties, requires understanding the other side's preferences/needs; thus, the agent needs to follow up its opponent's moves and accordingly make mutually beneficial offers. It has been shown that learning an opponent's preferences in negotiation is not trivial by any means [3; 27; 28]. For instance, non-exploratory behaviors, such as an opponent constantly making the same bids, prevents the agent from learning its opponent's preferences (especially in large-sized domains). The repetition results in overestimated preferences for repeated bids while underestimating preferences for less repeated or non-explored bids. Even when the opponent concedes, it should be taken into consideration that a concession at the start of the negotiation may not imply the same about the opponent's preferences as a concession towards the end. That is, while one may not concede on important issues at the start of the negotiation, this may prove false as the negotiation advances. As the opponent explores the bid space, one may be able to utilize the learned information to make better estimations, but a social agent should also be prepared for situations where the exploration of the bid space carried out by the opponent is scarce. In this article, we aim to find whether it is possible to design a social agent capable of negotiating efficiently in a competitive agent society. That is, while the agent should attempt to ensure an appropriate utility for itself, it should also strive to find a good agreement for the opponent. This should improve the long-term perspectives of the agent in a dynamic agent society. More specifically, we aim to answer the following research questions:Q1. Can a social agent obtain high utility in heterogeneous environments? Q2. Can a social agent achieve socially efficient agreements in heterogeneous environments? Q3. Can social negotiation strategies be an efficient choice for self-interested agents in agent societies? With these questions in mind, we designed an adaptive social strategy that considers both the opponent's moves and the context of the negotiation to adapt its social behavior accordingly. More specifically, our agent employs a portfolio of strategies supported by a new variant of the Tit-For-Tat strategy [29] that is based on the robust analysis of windows of opponent's bids and smooth transitions between target utility updates; a new frequency-based opponent modeling mechanism that aims to be robust against some of the aforementioned problems; and an adaptive behavior strategy that depends on the time pressure, the opponent's moves, and the information available about the opponent during the negotiation. The assumptions made by the proposed negotiation strategy are the following: (i) it assumes that agents' preferences are portrayed by linear additive utility functions; (ii) it assumes no previous knowledge or model about the opponents' preferences during the negotiation, with all the learning taking place from the information collected during the negotiation; (iii) it assumes bounded rationality by the agents and, therefore, agents employ heuristics rather than game-theoretic mechanisms to make decisions during the negotiation; and (iv) it assumes no particular behavior for the opponents.