# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 5 Future Directions
### Fuzzy Negotiation
The issues' values are also updated by \(\Delta x_{i}\) using an inference system where the weights assigned to them by the participants (\(w_{i}^{s}\) and \(w_{i}^{b}\) for the seller and the buyer respectively) are the inputs of the system and the output is \(\Delta x_{i}\), according to Equation (2): \[\Delta x_{i}=\frac{\sum_{j=1}^{n}z_{j}\cdot C_{j}}{\sum_{j=1}^{n}z_{j}},\] (2) where \(n\) is the number of rules of the FIS, \(z_{j}\) the firing strength of rule \(j\) as given in Equation (3): \[z_{j}=\mu_{significance}(w_{i}^{s})\otimes\mu_{significance}(w_{i}^{b}),\] (3) where the operator \(\otimes\) stands for the Fuzzy \"and\" and \(C_{j}\) is the consequence of each rule. Every issue has its significance, \(\mu_{significance}\), not necessarily the same, for every participant, and is expressed in linguistic terms as insignificant, neutral and significant. Therefore, the degree of significance could be represented as membership functions of Fuzzy sets, like the one in Figure 4:
3. Depending on the type of the participant (utility grid or smart building), the negotiation strategy can be adapted by an eagerness factor representing the urge of the seller or the buyer to sell or buy [19; 21]. Eagerness can be affected by multiple factors, as the time remaining, the history record of past transactions, the flexibility to curtail loads, etc. For short-time attitude, eagerness in a certain time, \(t\) for both buyer and seller can be expressed as in Equation (4): \[EA_{s}=\left\{\begin{array}{l}C_{s}\;if\;Con_{avg}<0.01\\ 1\  
from one round to another, differentiating the overall behavior of the participant in each round. Furthermore, q-ROFS can be integrated with other advanced intelligent methods to optimize negotiation outcomes in complex MCDM scenarios following the abovementioned strategies:  
* Enhancement of uncertainty representation. Future research could focus on q-ROFS refining to better capture and represent uncertainty in negotiation scenarios. * Improvement of DM precision. Since q-ROFS offer improved DM precision compared to conventional FL systems, future research could focus to the fine-tuning of the membership functions and similarity measures. * Dynamic adaptation to changing conditions. With the ability of q-ROFS to adapt dynamically to changing conditions in real time, which is essential in negotiation scenarios, future research could focus on developing q-ROFS models that can dynamically adjust their parameters and decision-making strategies based on evolving negotiation conditions. * Improvement of robustness and reliability. The combination of q-ROFS with other advanced intelligent methods results in decision-making systems that are more robust against errors and data inconsistencies. Future research could investigate techniques for integrating q-ROFS with machine learning algorithms to improve robustness and reliability in negotiation scenarios. * Exploration of a Wider Range of Potential Solutions. q-ROFS enhance exploration capabilities in the decision-making process by considering a wider range of potential solutions. Future research could focus on developing optimization algorithms that leverage the exploration capabilities of q-ROFS to identify better solutions in complex negotiation scenarios. Conclusively, integrating q-ROFS with MA offers significant advancements in DM capabilities for SG negotiation over conventional FL approaches. The key elements of this dominance are their enhanced representation of uncertainty, their improved DM precision, and the ability to search large solution spaces to find optimal or near-optimal solutions to complex, multi-objective problems representing negotiation scenarios. MA also offers  
Figure 4: Membership functions of the significance levels for an issue [28; 29]. Figure 3: Utility function of the issue “price”. dynamic adaptation to changing conditions in real time, which is essential in the SG context.