# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 4 Conclusions and Remarks
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[22] & Two Fuzzy decision controllers were designed. Fuzzy rules used to provide negotiation agents with accurate GMP values. & According to authors, the study faces two challenges; First, to evolve the negotiation agents’ structures by learning new relaxed criteria Fuzzy rules as they participate in more negotiations and secondly, to adopt the negotiation protocol to support coalition formations. \\ \hline
[23] & Fuzzy rules-based negotiation protocol adopting FL decision making scheme. FL determines the deadline of each interaction and the appropriate action at every round of negotiation. & MA such as GA may be applied to adapt to opponent’s behavior. \\ \hline
[24] & No AI implemented & The authors propose the utilization of Fuzzy Set Theory to introduce fuzzification to the intricate and ambiguous many-to-many supplier negotiation conditions. \\ \hline  & & This approach aims to develop a Fuzzy ES conducive to more pragmatic applications. \\ \hline  & & FTOPSIS has the ability to deal with different types of data, such as crisp, interval, Fuzzy or linguistic. It is computationally efficient and straightforward. Although the system is scoring system is stable, extreme offers may cause instabilities. Therefore, FTOPSIS calculations should be defined in appropriate manner, different than the traditional approach. IFIRP method allows the use of natural language, considers of subjectivity the lack of precision or uncertainties in the evaluation of negotiation offers. \\ \hline  & & Further experimental investigation is warranted to discern the negotiator’s approach to offer evaluation and the robustness of their reference points. Moreover, IFIRP should be empirically verified and DM should also testify about the behavioral decision-making aspect of the proposed approach. \\ \hline  & & The assessment of satisfaction and dissatisfaction levels and the recognition of reference points’ impact on the distance measure for the obtained ranking’s stability are also issues that may be studied thoroughly. \\ \hline  & & The FTOPSIS and IFIRP approaches could be useful in ill-structured problems, such as a negotiation process. \\ \hline
[27] & AFLS where new rules added by Simplex method. & Metaheuristics and NN may be employed to generate new rules for the AFLS and to define the rule base. \\  
Conclusively, the present study conducted a cross-disciplinary survey and the selected studies covered a wide range of cases where negotiation takes place. The majority of them employ FL theory as a tool to adapt to the opponent's behavior or to reach a decision-making inference. A small number of them employ or suggest metaheuristics such as PSO, GA, or BA mostly as a tool to refine Fuzzy rules allowing the agent to dynamically adapt to the opponent's strategy. A few numbers of the surveyed studies propose models where AI tools like ANFIS, NN, and metaheuristic algorithms can apply to future work. Despite the temptation to follow an already tested methodology applied in SG applications, authors recommend using these methodologies as benchmarks against novel implementations that may occur following and adapting one or more methodologies that haven't been applied yet in SG. Consulting the summary in Table 2, a scholar may step into a deeper search and propose new, more efficient models for SG and EM applications, or for markets with similar structures. In Section 3, the detailed Discussion of the surveyed studies reveals the limitations of these methods which are:  
1. The uncertainty handling, which is a primary challenge during negotiations. FL stands as a promising solution for handling uncertainty. However, it remains challenging to anticipate and cover all potential cases that may arise during a negotiation. More robust FL systems should be developed. 2. The incorporation of human psychological traits. Negotiation models often overlook the incorporation of human psychological traits such as risk aversion, patience, and regret.