# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 3 Discussion
### Comprehensive Review of the Selected Studies
The proposedmethod--according to the author--needs to be empirically verified about the usefulness of intuitionistic framework for negotiation support. DM should also testify about the behavioral decision-making aspect of the proposed approach. Important issues are also the development of techniques for the assessment of satisfaction and dissatisfaction levels for the negotiation options, and finally, the recognition of the reference point's impact or the distance measure for the obtained ranking's stability. An AFLS that participates in electronic marketplaces is presented in [27]. The AFLS is responsible for determining suitable actions in each round of negotiation. It relies on five input parameters and allows for the incorporation of new Fuzzy rules as needed, as well as the adjustment of membership functions. The simplex method plays a key role in the addition of new rules to the knowledge base, while an FL controller is responsible for the adaptation of the membership functions. Since this method lacks a reconstruction phase, the buyer can collect knowledge during successive negotiations, even if they refer to different items, in the same manner as humans do in real scenarios. The proposed model does not require complex modeling of the behavior of the buyer. It is not necessary to define a path to equilibrium or to perform complex calculations covering all the aspects of a negotiation. It generates, in addition, actions that fully adapt to the negotiation information. Furthermore, it does not require a training process. The negotiation model necessitated an effective decision-making mechanism capable of managing uncertainty. FL stands out as a promising solution for this task due to its ability to handle uncertainty. FL systems utilize a Fuzzy rule base that addresses various aspects of an entity's behavior, with this rule base typically defined with input from experts. Nevertheless, it remains challenging for experts to anticipate and cover all potential cases that an entity may face during automated negotiations. In [29], a multi-demand negotiation model based on Fuzzy rules elicited via psychological experiments with an example in politics, where two parties negotiate over policies is presented. The model engages in multi-demand negotiations within discrete domains, effectively capturing human psychological traits such as risk aversion, patience, and regret. This is achieved through the utilization of Fuzzy rules to determine the extent to which a negotiating agent should adjust their preference structure, as elicited from psychological experiments. The study elucidates how human psychological traits pertaining to risk, patience, and regret influence alterations in preference structures throughout the negotiation process, and also identifies the conditions conducive to reaching an agreement in bilateral negotiations. Fuzzy rules were deployed to represent degrees of \"risk\", \"patience\", and \"regret\" factors, and a standard Mamdani method of FL inference was utilized to dynamically calculate the extent to which an agent's preferences should be adjusted based on factors such as risk, patience, and regret during the negotiation process. Empirical analysis was conducted to determine the influence of attitudes towards risk on negotiation outcomes and to demonstrate the superior performance of the FL-based model in terms of negotiation success rate, efficiency, and quality compared to a well-known model. Furthermore, the model's efficacy in resolving a negotiation problem within the political domain is illustrated. In [30], Atanassov intuitionistic Fuzzy constraints-based approach for offer evaluation during the course of a negotiation is proposed. According to the study, an offer is evaluated through three steps: First, prioritized Atanassov intuitionistic Fuzzy constraints are used to characterize the agent's goals.