# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 4 Conclusions and Remarks
The authors propose the utilization of Fuzzy rules to determine the extent to which negotiating agents should adjust their preference structures based on these \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Method** & **Comments and Suggestions** \\ \hline \multirow{4}{*}{[31]} & A Sugeno-type FIS was devised to ascertain the appropriate level of concession, weighing preferences and utilities of negotiating parties in trade-off decisions. Opponent’s behavior is estimated using a tradeoff algorithm based on Fuzzy similarity. & As per the authors, certain parameters need to be appropriately initialized prior to commencing negotiations to optimize performance. \\ \hline \multirow{4}{*}{[32]} & The system demonstrates flexibility, since it has the capability to have logical pattern in proposals’ offering. & Since the proposed technique in depending on the initial estimation of the opponent’s preferences, supplying precise initial approximations utilizing techniques from natural language processing should be studied. Auxiliary data can be also used to generate pseudo-observations for the UPF module, improving further the estimations. \\ \cline{2-3}  & Buyer and seller agent models are defined by utility functions. Heuristic strategies are followed to choose an offer. & Authors future research targets include the enhancement of the method in many-to-many negotiations scenarios. Agent models may be implemented by ANFIS. \\ \hline \multirow{4}{*}{[34]} & Real time TDR algorithm using Fuzzy inference engine to satisfy the users’ comfort levels and preferences responding to rewards offered by the TA. The customers were classified using k-means clustering, & The proposed scheme is a realistic approach for vendors and solution providers to achieve the SG vision. \\ \hline \multirow{4}{*}{[35]} & A MOBA, a Pareto front and a FDM algorithm for managing the energy trading between prosumers. & The proposed methodology was tried against a similar MOPSO algorithm and promises to facilitate autonomous energy transactions, thus establishing an intelligent energy market. Subsequent research endeavors should analyze and substantiate the algorithm’s optimality and convergence. \\ \hline \multirow{4}{*}{[37]} & q-Rung Orthopair Fuzzy Aczel Alsina Triangular Norms and Conorms sets based Aggregator Operators implemented in a MCGDM algorithm. & The proposed approach outperforms other q-ROF based AO as well as AO relying on Pythagorean and Intuitionistic Fuzzy sets. They can be applied to a more extensive range of information. The proposed AO generalize the previous AO. \\ \hline \multirow{4}{*}{[38]} & Application of BPSO in P2P trading. MA are suggested as a prevalent part of AI for solving optimization problems. & The proposed approach outperforms other q-ROF based AO as well as AO relying on Pythagorean and Intuitionistic Fuzzy sets. They can be applied to a more extensive range of information. The proposed AO generalize the previous AO. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._psychological traits. This involves eliciting psychological experiments to inform the Fuzzy rules accurately. Future research could delve deeper into understanding how these psychological traits influence negotiation outcomes and develop more sophisticated Fuzzy rule bases to capture human behavior effectively. 3. The evaluation of negotiation offers. Evaluating offers during negotiations is a complex task, especially considering the diverse preferences and constraints of negotiating parties. The authors propose an Atanassov intuitionistic Fuzzy constraints-based approach for offer evaluation, considering satisfaction and dissatisfaction levels. This method aims to represent human-like behavior during the negotiation process and allows for the consideration of various negotiation objectives.