# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 3 Discussion
### Comprehensive Review of the Selected Studies
This algorithm empowers agents to navigate trade-offs involving both discrete and continuous decision variables, particularly in scenarios involving information uncertainty and resource constraints for multi-dimensional commodities. Employing Fuzzy similarity, the algorithm approximates the opponent's structure of preferences in the negotiation and employs a hill-climbing technique to investigate the possible range of trade-offs, aiming to identify the most probable acceptable solution. Despite the algorithm's applicability being limited to linear problem domains, the abstract structure of the underlying similarity model serves as a component of the overall negotiation algorithm, making it adaptable for use by any negotiating agent. The algorithm has undergone not only theoretical analysis to assess its complexity but also empirical evaluation to gauge its operational performance. Theoretical analysis has revealed that the algorithm's average complexity is linearly correlated with the number of negotiation decision variables under consideration. The empirical findings, using a car dealing negotiation as an illustration, showcased the algorithm's efficacy in generating trade-offs across various negotiation scenarios. Notably, it highlighted the correlation between the algorithm's exploration of potential outcomes and its capacity to yield agreements with enhanced joint gains. This amplified search for higher joint outcomes in each iteration of the algorithm, whether in a singular or multiple environment, also led to increased communication costs due to the necessity for more proposals before reaching an agreement. The crafted computational model using heuristics to represent trade-off strategies holds the potential to augment the social welfare of the system. The opponent's preference structure is also used in [17], as the historical data from which the system learns. This study outlines the negotiation tactics of agents within e-marketplaces, presented as Fuzzy rules, wherein agents learn from past proposals aiming at estimating the opponent's preference framework This approach aims to improve subsequent negotiation rounds by enabling the formulation of more promising offers. Specifically, FIS are employed to conduct heuristic searches, facilitating the learning of opponent issue weights. Furthermore, agents' trade-off strategies are explicitly articulated as FIS, allowing for the generation of new offers in each negotiation round based on issue importance levels and the opponent's previous offer. The experimental results indicate that the performance of the proposed approach meets satisfactory criteria regarding negotiation duration, the attained joint utility, and the Pareto efficiency of the negotiated agreements. Nevertheless, the authors propose the necessity for a more refined learning scheme to improve the accuracy of the agent's predictions regarding the opponent's preference structure. In [18], the agents were categorized into two groups: matching agents and negotiation agents. Matching agents pair buyers and suppliers by identifying the M most analogous proposals to each participant in an e-marketplace, sorting the number of the opponents who a negotiation agent has to negotiate with, to a few who are considered the most promising. This method operates on the premise that dyads with a high matching degree are more likely to efficiently reach agreements in subsequent negotiations. This reduces the occurrence of fruitless negotiations, thereby enhancing the success rate of contracts. When matched agents inform a participant about a set of potential opponents and their offers, the participant activates its negotiation agent to engage with each of them. The agent can either decline an opponent's offer or present a counteroffer. The strategies employed by agents during trade-offs are structured as FIS, enabling the generation of new offers in each negotiation round.