# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 2 Methodology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Title/Author(s)** & **Commodity/ Application** & **Method** \\ \hline
2013 &
\begin{tabular}{c} “Adaptive Negotiation Agent for \\ Facilitating Bi-Directional Energy Trading \\ Between Smart Building and Utility Grid” \\ ([PERSON] and [PERSON]) \\ [21] \\ \end{tabular} & Smart Building and Utility Grid Bidirectional Energy Trading & AABS-optimized with PSO (PSO-AABS) \\ \end{tabular} \\
2014 & \
\begin{tabular}{c} “A new Fuzzy negotiation protocol \\ for grid resource allocation” \\ ([PERSON] et al.) \\ [22] \\ \end{tabular} & Market-based grid resource allocation problem & Enhanced Market and Behavior Driven Negotiation Agent (EMBDNA) with a Fuzzy negotiation protocol aiming to improve \\  & [23] & Allocation problem & [PERSON]’s sequential alternating offer protocol and develop two novel Fuzzy Grid Market Pressure Determination Systems. \\ \end{tabular}
\begin{tabular}{c} “Sellers in e-marketplaces: A Fuzzy Logic \\ based decision support system” \\ ([PERSON], [PERSON] \\ and [PERSON]) \\ \end{tabular} & e-marketplaces & A decision-making model which utilizing a FL-based negotiation protocol, centering on the seller’s behavior and implementing an FL-based decision-making approach without prior knowledge of the opponent’s characteristics. \\ \hline
2014 & \
\begin{tabular}{c} “Development and evaluation \\ of the many-to-many \\ supplier negotiation strategy” \\ ([PERSON]) \\ [24] \\ \end{tabular} & Supply chain. Many-to-many supplier negotiation process under different order and competitive conditions. & The interactive bidding strategy discussed in this paper is a variation inspired by the prior research conducted by [PERSON] and [PERSON] [25] where a bidding strategy is proposed in a Supplier Selection Auction Market (SSAM). To fulfill its objectives of supplier selection under fluctuating order conditions, a party engaging in negotiation must attentively consider fundamental concepts such as negotiator’s power, congestion dynamics, target and reservation prices, time constraints, and the principle of equitable concessions. \\ \end{tabular}
\begin{tabular}{c} “Supporting Ill-Structured \\ Negotiation Problems” \\ ([PERSON], [PERSON] \\ and [PERSON]) \\ \end{tabular} & Media Reference Model for electronic negotiation context, with four major phases of interaction, known as the Montreal Taxonomy. & Modified Fuzzy Technique for Order Preferences by Similarity to Ideal Solutions (FTOPSIS) and Analytic Hierarchy Process (AHP). AHP derives the negotiation issue weights instead of the classic approach of direct assignment of values to the issues. FTOPSIS is employed in constructing the final scoring system, enabling the assessment of all potential negotiation packages. \\ \hline
2015 & \
\begin{tabular}{c} “An adaptive Fuzzy logic system \\ for automated negotiations” \\ ([PERSON], [PERSON] \\ and [PERSON]) \\ [27] \\ \end{tabular} & e-marketplaces & An Adaptive Fuzzy Logic System (AFLS) guides suitable actions throughout each negotiation round. Additional rules are integrated into the knowledge base through a model where the Simplex method assumes a central role. An FL controller dynamically adjusts the membership functions. \\ \end{tabular}
\begin{tabular}{c} “Application of Fuzzy TOPSIS to \\ scoring the negotiation offers in \\ ill-structured negotiation problems” \\ ([PERSON] and [PERSON]) \\ \end{tabular} & General buyer-seller example. & FTOPSIS facilitates the assessment of negotiation offers, creating a hierarchy of compromise solutions, formulating counteroffers, and assessing and contrasting potential concessions. Moreover, it aids in enhancing negotiation compromises by seeking Pareto optimal and dominant solutions. \\ \hline
2017 & \
\begin{tabular}{c} “A multi-demand negotiation model \\ based on Fuzzy rules elicited via \\ psychological experiments” \\ ([PERSON] et al.) \\ [29] \\ \end{tabular} & Political example. Negotiation over \\  & & policies between two parties. \\ \end{tabular}
\begin{tabular}{c} The extent to which a negotiating agent adjusts \\ its preference structure based on risk, patience, \\ and regret is determined through Fuzzy rules \\ derived from psychological experiments. \\ \end{tabular} \\ \hline
2018 &
\begin{tabular}{c} “An [PERSON] intuitionistic Fuzzy \\ constraint-based method for offer \\ evaluation and trade-off making \\ in automated negotiation” \\ ([PERSON], [PERSON] and [PERSON]) \\ [30] \\ \end{tabular} & Business & An approach based on [PERSON] intuitionistic \\ \end{tabular}
\end{table}