Multi-Agent Paradigm for B2C E-Commerce

Abstract

Various classical as well as modern intelligent computing methods such as knowledge-based systems (KBS), case-based reasoning (CBR), artificial neural network (ANN) and genetic algorithm (GA) have been deployed to implement the various functionalities of e-business. Multi-agent systems (MAS) have also been used to represent the buyers and sellers as agents and the broker as a coordinator agent. In this chapter the definitions of salient features, functions, procedure and methodology of various concepts pertaining to business and computational perspective of Artificial Intelligence have been described. These computing models are AI based and multi-agent systems which are incorporated with brokering and customer orientation. Machine learning and deep learning integrated MAS methods have also been described in this chapter. Various illustrations describing the business applications of KBS, CBR, ANN, GA and MAS have been discussed in this chapter along with negotiation, customer relationship management and customer orientation. From computational point of view, agent characteristics, multi-agent system paradigm and its communication protocol have been discussed. The Belief-Desire-Intention (BDI) architecture for mental state and other cognitive parameters such as preference, commitment, and capability required for computation of trust in any AI-based e-business system to formalize the internal architecture of complex agents. The description of prominent characteristics, functions, procedures and methodologies of different market and computational perspective principles have been provided in this chapter. These computational models are AI-based applications, Multi-Agent applications that are paired with negotiation and customer focus. Integrated MAS methods of Machine Learning have also been discussed in this chapter.