# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 3 Discussion
### Comprehensive Review of the Selected Studies
The AABS-based negotiation model simulates traders' eagerness and behavior, employing a mathematical function for the representation and computation of eagerness. Dynamic factors like comfort level and battery SOC significantly influence the negotiation's outcome. In [20], a desire-based negotiation model is proposed based on mathematical models. Again, the agents' behavior is influenced by their desire to complete the negotiation, similar to the eagerness concept in [19]. The model facilitates agents in exhibiting adaptive negotiation behaviors within e-markets, considering both the e-market's context and the agent's preferences. Additionally, a negotiation protocol is introduced to outline the negotiation process within e-markets. The experimental results convincingly showed that the proposed model adeptly adapts to changes in the e-market's conditions, thereby modifying agents' negotiation behaviors accordingly. Furthermore, drawing from the experimental results, the concept of a \"trading surface\" is introduced, highlighting the enhanced applicability of the proposed negotiation model compared to traditional models in e-markets. Two significant aspects of the study stand out: Firstly, the investigation into expanding the current model from single-issue negotiation to multi-issue negotiation, integrating both numerical and categorical factors. Secondly, the investigation into leveraging Fuzzy and NN approaches to bolster agents' adaptive negotiation behaviors. A resource management of shared computing resources protocol is presented in [22], where negotiation between Grid Resource Owner (GRO) and Grid Resource Consumer (GRC) takes place to successfully reconcile the differences between GRO and GRC. The proposed protocol emphasizes enhancing the alternating offers protocol by introducing two new Fuzzy decision controllers. One controller models the criteria of GRO, while the other models those of GRC. Again, the concept of eagerness is underlying the concept of Grid Market Pressure (GMP). The study's distinguished features are the presentation of the GMP value determination approach, in which sets of Fuzzy rules are employed to furnish negotiation agents with a more precise GMP value, specifically in terms of the degree of relaxation. Moreover, the mentioned work introduces the EAlternating offer protocol, an improvement upon [PERSON]'s sequential alternating offer protocol proposed in [39; 40]. This protocol is designed to handle multiple trading opportunities and market competition, ensuring control over invalid behavior and relaxation of bargaining criteria for negotiation agents based on the GMP value. Finally, this study proposes a new design of negotiation agents, namely EMBDNA augmenting Market and Behavior Driven Negotiation Agents (MBDNA)proposed in an older work of the authors [41], which were not adopting the proposed Fuzzy negotiation model and were not relaxing their bargaining term in intense GMP conditions with the proposed negotiation protocol. The experiments conducted for the evaluation of the performance of GRC under various market types, densities, grid loads and deadlines, demonstrated that GRC EMBDNA took shorter negotiation time on average while having higher success rates, and achieved higher expected utilities than MBDNA and GRC Enhanced Market Driven Agent (EMDA), a negotiation agent design which adapts its bargaining terms when faced with significant GMP, considering two relaxation factors. Furthermore, GRO EMBDNA typically exhibited shorter average negotiation times, higher success rates, and superior resource utilization levels compared to both MBDNA and EMDA counterparts. A decision-making model is proposed in [23], which embraces an FL-based negotiation protocol tailored for electronic marketplaces. In a bilateral single-issue scenario, a buyer and a seller engage in exchanging offers with the aim of reaching an agreement on the price of a product. The emphasis is on the seller's behavior, and an FL-based decision-making scheme is adopted.