# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 3 Discussion
### Comprehensive Review of the Selected Studies
Next, the overall satisfaction and dissatisfaction degrees of an offer according to the beforementioned constraints reflect the hesitation degrees of the users when estimating the satisfaction degrees of all relevant constraints. Lastly, the agents' acceptability of an offer is calculated with aggregation of both overall satisfaction and dissatisfaction as well. Calculating the acceptability of the offer leads to the determination of the agents' decision to accept an offer or deny and make a counteroffer instead. Atanassov intuitionistic Fuzzy constraints can represent human-like behavior during a negotiation process, like the goals they have during the process, the human cognitive limitations, and the knowledge that leads to the hesitation to accept an offer. The contribution of this tradeoff method lies in its consideration of not only the similarity to the previous offer when crafting a counteroffer but also the satisfaction and dissatisfaction levels pertaining to its most prioritized goal. Moreover, in this method, the weights are set to embody the disparities in opinions on the importance of these three factors between different users. Furthermore, it is necessary to develop diverse forms of satisfaction and dissatisfaction functions to represent the individual characteristics of users and to analyze how these variances impact the results of a negotiation. In this study, the functions representing satisfaction and dissatisfaction degrees concerning various negotiation objectives are presumed to be defined by users of negotiating agents or chosen from a predetermined function set within a negotiation system. Establishing the functional expression of Fuzzy constraints, or acquiring Fuzzy constraints, poses a critical challenge that must be addressed when implementing the proposed model into practical negotiation systems. Two examples from the business sector where buyer agents in an accommodation renting scenario illustrated how the model works, showing that this method can also be applied in both cases of independent and interdependent multi-issues. As a result, integrating various concession strategies and the proposed trade-off approach into a single negotiation system may be beneficial. When integrated with the suggested trade-off decision-making method, a negotiating agent may identify a suitable compromise before conceding utility, thereby enhancing the likelihood of reaching an agreement. Consequently, in scenarios with numerous alternative trade-off proposals, it becomes necessary to devise an algorithm capable of swiftly identifying the most suitable trade-off within a multi-issue negotiation environment. A mixed-strategy ANS for a CCSN is recommended as a strategy in [31]. In this study, a new negotiation strategy was designed and simulated aiming to maximize utility for both trading parties while the speed in reaching an agreement is increased. Moreover, a process for the aggregation of the results of the negotiations on simple task requirements to ensure end-to-end composite service requirements is proposed. Proposals are generated based on factors such as remaining time, market competition, and opponent's behavior which are environment-related factors, and preferences and utilities which are negotiator-related factors. These factors also can be divided into two categories considering their effect on the proposal generation, namely concession and tradeoff factors. A simple FIS was devised to gauge the appropriate level of concession, considering the preferences and utilities of negotiating parties, thereby striking a balance between preferences and achieved utilities to ascertain the extent of concession. Sugeno-type FIS is preferable because avoids time-consuming defuzzification in FIS which has Fuzzy consequences in its rules. The opponent's behavior is estimated using a tradeoff algorithm based on Fuzzy similarity, where a proposal that has more similarity with the opponent's proposal has a better chance of being accepted by the opponent.