# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 3 Discussion
### Comprehensive Review of the Selected Studies
As a mixed strategy, the proposed framework maximizes the utilities of the negotiating parties in the shortest possible time. Simulation results show that negotiator-related factors, market competition, and opponent's behavior affect the performance's enhancement. Opponent learning in real-time automated negotiation is the concept of the methodology presented in [32]. In the context of multi-participant and multi-issue negotiation, an estimation technique based on Fuzzy functions models the opponents' preference profiles, since Fuzzy functions are very close to the human's way of evaluating alternatives. During the negotiation, the agents learn the parameters of these models in real time recursively, using a Bayesian approach based on Unscented Particle Filtering (UPF). In UPF, the posterior distribution of the state is recursively estimated by a set of particles (samples) that evolve over time, conforming to the system's transition and observation models. In each round, the proposer agent offers a proposal to stakeholders. Stakeholders respond to the proposer by sending feedback which includes a Fuzzy score assigned to the proposal and some arguments about the proposal. Using the feedback, the preference model is recursively estimated with the UPF approach. The estimated preference model is used by the proposer to find the next proposal that is closer to opponents' preferences while at the same time meeting his criteria as well, enabling a Belief Propagation Proposal Preparation (BPPP) module. Therefore, the proposals in each round have a higher probability of being accepted by the stakeholders. This technique facilitates the process of negotiation and accelerates reaching an agreement. The proposed methodology was compared to a frequency-based approach using two different case studies: The planning of an energy system in Alberta and in real estate sales in King County, where the UPF methodology accelerated the process to 85% when realistic knowledge of the users was available and up to 95% when no prior knowledge about the users was available. The uncertainty of the estimation is considered by the proposed approach which makes it capable of having a logical pattern in offering proposals. For example, when stakeholders show strong concerns about an attribute, the BPPP module opts for a new proposal significantly divergent from the preceding one. Moreover, the effectiveness of the proposed estimation technique relies on the initial approximations of the opponents' preferences. Enhanced initial estimations result in faster convergence of the UPF towards the actual preference profile of the user. The authors' future work aims to provide more accurate initial approximations using natural language processing techniques. Also, another future goal is to use the auxiliary data to generate pseudo-observations for the UPF module, improving the estimations even more. A one-to-one automated negotiation framework applied in local energy markets is proposed in [33] for P2P local trading of electricity. This framework utilizes an autonomous agent model to encompass the preferences of both sellers and buyers regarding the price and quantities of electricity to be traded across various periods within a day. A bilateral negotiation framework is established, drawing from [PERSON]'s alternating offers protocol. This framework aggregates the electricity quantity and price for different time periods into daily packages, which are then negotiated between the agents representing the buyer and seller. Buyer and seller agent models are defined by utility functions. The study examines three negotiation strategies: Zero Intelligence, Boulware, and linear heuristic. The heuristic strategies select an offer from a narrowed set of viable packages. The proposed agents, employing these strategies and the negotiation protocol, were deployed in a case study involving a prosumer equipped with a solar home system and a battery, as well as various consumers with diverse energy and price preferences.