# An Intelligent System to Ensure Interoperability for the Dairy Farm Business Model
## 7 Discussion  
Many research studies have emphasized the importance of automated negotiation as a key role in dynamic trading in e-commerce. In this context, several papers have addressed the issues of selected negotiation strategy and specific negotiation protocol [51; 52; 53; 54; 55; 56]. For example, based on previous negotiations, [PERSON] [54] introduced an automated negotiation agent that could predict the strategies of the partners. Likewise, [PERSON] et al. [56] tackled the problem of coordination of multi-party, multi-issue negotiations and proposed a protocol and rules that support agents to manage their interactions and reach an agreement. However, many of these studies did not consider an important feature of human behaviour: the ability to express preferences in the negotiation process. In addition, other approaches assume that the agent is able to provide information about the history of previous negotiations. Regarding the design of the negotiation environment, there are two main directions: (i) automated negotiations in which software agents replace human users in the negotiation process and (ii) semi-automated negotiations in which software agents assist and support human negotiators in the course of the negotiations or directly negotiate with a human user (i.e., agent-to-human interaction). Concerning the first direction, [PERSON] et al. [57] proposed a conceptual framework for achieving automated service level agreements (SLA) negotiation in which intelligent agents represent cloud providers and consumers. However, the negotiation coordination approach proposed herein is based on a one-to-one protocol in which an agent makes a proposal and the other agent can either accept or reject it without being able to respond with a counter-proposal. Likewise, a recent study by [PERSON] and [PERSON] [58] proposed an automated negotiation system for hybrid cloud computing that allows for concurrent negotiation transactions with multiple opponent agents. However, this approach has only one owner of the IT infrastructure looking to scale its infrastructure into the cloud. In addition, the paper focused on a single negotiation attribute (i.e., cost value) for each agreed issue and a time threshold. A second direction has been found in many research studies [55; 57; 58; 59; 60]. However, there have been few studies that apply automated negotiations to real-world problems. For instance, [PERSON] et al. [60] addressed the issue of multiple equivalent simultaneous offers for e-commerce transactions by proposing an efficient offer strategy model to achieve a win-win negotiation for an automated negotiation agent. This approach exhibits three attributes (issues) of the negotiation task over which agreement must be reached (e.g., unit price, warranty, and delivery date). The main difference of the automated business model presented in this paper is in the proposed solution's ability to coordinate multiple concurrent negotiations. Hence, we proposed a solution that splits the coordination issues into two groups depending on the complexity of negotiations (i.e., components in open and closed en  
Figure 11: Negotiation graphs for the three guests: (**a**) DF2, (**b**) DF4, and (**c**) DF6. vironment). Based on this distributed approach, we developed a parallel negotiation coordination model distributed over several coordination components. The proposed solution allows one to cope with a scalable environment and easily decrease/increase the complexity of coordination by removing/adding independent coordination components. Moreover, the division of the coordination model into independent coordination components makes it very flexible, thus allowing it to be used for simple interoperability negotiations (i.e., bilateral negotiation on simple attributes and with limited number of interactions) up to very complex interoperability problems (i.e., multiple participants, complex attributes, and dependent or nested negotiations).