# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 3 Discussion  
### Comprehensive Review of the Selected Studies  
In [14], the author introduces soft constraints that adequately address problems where not all of the constraints need to be satisfied. More specifically, constraints or sets of constraints are compared on the basis of certain criteria, such as, for example, weights, which indicate their importance which is tied to given crisp importance. These \"soft\" constraints generalize conceptually the idea of crisp constraints. Since crisp constraints are characterized by their according set of tuples for which the constraints apply, it is entailed that Fuzzy sets characterize the according Fuzzy constraints, where different tuples satisfy on a different degree a given constraint. A generalization of classical solving heuristics which are used in classical CSP can be used to control the solution construction process of the Fuzzy CSP solving. Branch and bound search can replace the abovementioned heuristic search. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Title/Author(s)**} & **Commodity/** & \multirow{2}{*}{**Method**} \\  & & **Application** & & \\ \hline \multirow{4}{*}{2019} & \multicolumn{2}{c}{\({}^{a}\)Automated negotiation for} & \multirow{4}{*}{Cloud services} & \multirow{4}{*}{Mixed strategy ANS for} \\  & & ensuring composite service requirements & & \\  & in cloud computing\({}^{a}\) & & & Cloud Computing Service Negotiation (CCSN). \\  & ([PERSON], [PERSON] and [PERSON]) & & & \\  & ([PERSON], [PERSON] and [PERSON]  
This study provides novelties such as a good and general framework of mathematics defining Fuzzy constraints, Fuzzy Constrained Satisfaction Problems (FCSP), and the degree of satisfaction of solutions. It also shows that the classical solution method can also be applied to FCSP cases. An example of a robot facing the problem of choosing matching pieces of clothing when trying to get dressed can be illustrated as a FCSP. The level of compatibility among potential combinations is represented by the satisfaction level of corresponding binary Fuzzy constraints. This collective satisfaction adheres to the principle of \"production combination\". The suggested heuristics can be implemented with any definition of solution degree that meets the criteria of independent computation and monotonicity. Additionally, further enhancement of the branch and bound search is possible by integrating heuristics for prioritizing constraint verification. In [15], a GA-agent ANS with application on e-commerce is proposed, in order to minimize the required user input. The negotiation model studied involves a bilateral, multi-issue negotiation. Multi-issue negotiation involves reaching an agreement encompassing multiple terms. The system acquires knowledge of the opponent's preferences through observing counteroffers and adjusts to the environment by dynamically modifying its mutation rate. The proposed structure proactively anticipates user needs, initiating a purchase process. The agent proposes a set of default parameters and seeks confirmation from the owner, considering the user's profile and schedule. Negotiation can then adopt a suitable strategy based on the deadline and user preferences. The novelty of the suggested structure is that, unlike other GA-based negotiation agents, the proposed agent attempts to learn the opponent's preference using dynamic mutation rate modification following a rule wherein the mutation rate is directly linked to the logarithm of the population size but inversely related to the goodness value. Therefore, as the goodness value increases, a lower mutation rate should be employed. The proposed structure can be used as an online ANS. In [16], a linear algorithm is presented to tackle the complexities of multi-issue negotiation, including the challenging task of balancing decision variables.