# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 4 Conclusions and Remarks  
The present work attempts to focus on an important aspect of the SG concept and provide a critical review of selected works which, in the authors' opinion, may give scholars and researchers the spark to adapt the presented methodologies and concepts to their work, or to enhance the reviewed methods with new features and approaches. Some interesting points were:  
* The concept of imitating eagerness and other human-like attributes like hesitation, the willingness to risk, etc. with Fuzzy sets. * The dominance of FL in tools able to learn tactics, and make trade-offs. * The Intuitionistic Fuzzy sets stability and applicability in ill-structured problems like negotiations. * The Bayesian UPF approach which offers flexibility to the negotiation offers in [32]. * K-means clustering customer's classification and Fuzzy inference engine to satisfy customer's preferences in response to rewards offered by the TA in an SG context as described in [34]. * The general description of a system applied in energy trades and the use of novel metaheuristic algorithms such as BA in [35]. * The q-ROFEMA OA dominance in comparison to other q-ROF-based OA in [37]. * The potential of integrating FL, Bayesian approaches, UPF, and NLP techniques into negotiation frameworks, where agents can enhance the robustness and reliability of negotiation methods in coping with the variability and uncertainty of renewable energy sources. In most cases, AI approaches were implemented, while in others, AI was partially employed or had the potential to integrate with the presented methodology. Some may apply to one or two of the cases referred to in Section 2, while some may, with the proper arrangements, apply in all three cases. In Table 2, a summary is given. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Method** & **Comments and Suggestions** \\ \hline
[14] & Fuzzy sets to characterize Fuzzy constraints instead of crisp. & A metaheuristic approach may replace the Branch and \\  & Branch and Bound search to control the solution construction & Bound search. \\  & process of the Fuzzy CSP solving. & \\ \hline
[15] & GA is employed to train dynamically the agent to & Other metaheuristic approaches may be used instead of GA. \\  & adapt on the opponent’s preference. & \\ \hline
[16] & Fuzzy similarity approximator to approximate & \\  & the opponent’s structure of negotiation. & Hill climbing technique to explore the potential trade-off \\  & space for the most possible acceptable solution. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A summary of the presented works and their potential use. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Method** & **Comments and Suggestions** \\ \hline
[17] & FIS are used to learn the opponent’s tactics. & Since the authors suggest that a more delicate learning scheme is required to enhance agent’s prediction accuracy, an Adaptive Neuro-Fuzzy Inference System (ANFIS) or a metaheuristic algorithm to tune the FIS may apply. ANFIS are well known for their predictive accuracy along with Neural Networks (NN). \\ \hline
[18] & FIS formulate the tactics of the agents when making trade-offs and used to infer new offers at each negotiation round. & The authors suggest a more flexible utility function in order to be this approach more useful in real Business-to-Business environment. \\ \hline
[19] & Fuzzy controller calculates the required power under dynamic circumstances. The model is based on mathematical functions and not AI except the controller. \\ \hline
[21] & Enhancement of the model in [19], integrating PSO to overcome the difficulty the previous approach had in generalization. & The authors suggest GA and NN technologies to employ, and the most suitable of them to be found by comparison. \\ \hline
[20] & The proposed approach didn’t include any AI implementation, relying on mathematical models. & The authors consider to employ Fuzzy and NN approaches to enhance the agent’s adaptive negotiation behaviors.