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In this chapter, we consider the model of call center incoming call forecasting and staffing-level optimization. We first present the structure of the model and how an agent-based modeling technique could enrich the decision rule and the model. A matrix layout is introduced to present the model so that it can be understood in an efficient way from the perspective of a programmer. The agent-based queuing model will be used in forecasting. We then utilize the bisection method and stepwise method to optimize the staff level to satisfy a target range service-level criteria. Call center management could use the model in practice for their management forecasting and optimization decision-making process in terms of how many agents they need to achieve the target business efficiency goal.

A vast body of literature has documented the negative consequences of stress on employee performance and well-being. These deleterious effects are particularly pronounced for service agents who need to constantly endure and manage customer emotions. The purpose of this paper is to introduce and describe a deep learning model to predict in real-time service agent stress from emotion patterns in voice-to-voice service interactions.

A deep learning model was developed to identify emotion patterns in call center interactions based on 363 recorded service interactions, subdivided in 27,889 manually expert-labeled three-second audio snippets. In a second step, the deep learning model was deployed in a call center for a period of one month to be further trained by the data collected from 40 service agents in another 4,672 service interactions.

The deep learning emotion classifier reached a balanced accuracy of 68% in predicting discrete emotions in service interactions. Integrating this model in a binary classification model, it was able to predict service agent stress with a balanced accuracy of 80%.

Service managers can benefit from employing the deep learning model to continuously and unobtrusively monitor the stress level of their service agents with numerous practical applications, including real-time early warning systems for service agents, customized training and automatically linking stress to customer-related outcomes.

The present study is the first to document an artificial intelligence (AI)-based model that is able to identify emotions in natural (i.e. nonstaged) interactions. It is further a pioneer in developing a smart emotion-based stress measure for service agents. Finally, the study contributes to the literature on the role of emotions in service interactions and employee stress.

In view of the particularity of innovative product diffusion under the background of market competition, this paper firstly uses consumer behavior theory to logically deduce the dynamic process of consumer behavior from the perspective of experience theory.

Bass and Lotka-Volterra model are used to describe and model the consumers' perceptual behavior in competitive environment. On this basis, interactive modeling technology is used to model and simulate the diffusion process of innovative products. Finally, the validity of the model is verified by comparing two scenarios with an example.

The research shows that the stronger the enterprise's competitiveness, the higher the market share of innovative products, and the positive impact on consumer perceived value, consumer perceived value can promote consumers' second purchase behavior. Positive word-of-mouth and advertising positively affect consumers' purchasing decisions; negative word-of-mouth negatively affects consumers' purchasing decisions.

The interaction modeling technology and AnyLogic software platform are used to simulate the complexity of consumers' experiential perception so as to build the interaction and competition mechanism among different Agent, which realizes the dynamic simulation of the diffusion process of innovative products. This study provides guidance for enterprises to formulate effective marketing strategies.

The purpose of this paper is to create a platform framework based on agent for virtual enterprise (VE) with the characteristic of supply chain by adopting multi‐agent technology.

According to the system hierarchy theory and the features of VE with the characteristic of supply chain, the conception and organization structure of supply chain‐oriented VE are proposed. Combined with characters of multi‐agent, a platform framework based on agent for VE with the characteristic of supply chain is created. Aiming at complexity of net node of running platform framework, two‐layer architecture mode, which are information alternation layer and basic function layer inside net node based on agent, are designed. The modes are proved to be reasonable in the management of the system and networks resources.

The theory base of realization of the VE based on agent with the characteristic of supply chain is found.

The reasonable basic function design of various agents are main limitations.

The paper presents a very useful tool for the operation and management of VE.

A new approach and scheme for VE with the characteristic of supply chain is presented. This paper is aimed at researchers and engineers.

First this paper introduces the concepts of virtual manufacturing system (VMS). The host enterprise and the multiple numbers of supply and distribution enterprises that make up a VMS, and the hierarchical and horizontal relationship that exists between these enterprises are explained. The steps involved in formation and operation of a VMS are then analyzed in detail. Second, we present a three view based methodological approach to make a multi‐agent model of VMS. Finally, with the help of a testing prototype, we show how to develop an autonomous Internet based data collection system for operation of VMS in accordance with the proposed methodological approach.

This article describes the use of intelligent agents to filter and categorise information and concludes with case studies of select Verity partners who have built unique applications using Verity agent technology.

The mythological ‘Daily Me’. At the recent NetMedia 97 conference at City University I watched a presentation by Steve Yelvington, Editor of the Star Tribune Online in Minneapolis. He was describing the development of an intelligent agent system called MOM (My Own Matrix) that the Star Tribune is developing to serve the needs of its readers.

Fairly rapid environmental changes call for continuous surveillance and on‐line decision making. There are two main areas where IT technologies can be valuable. In this paper we present a multi‐agent system for monitoring and assessing air‐quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air‐quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data‐driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real‐world test case is demonstrated.

One of the greatest challenges facing any intelligent tutoring system is being able to adapt its behaviour based on the student's current knowledge level, ability, needs and wishes within a course. This paper aims to present a framework of BDI agents within an agent‐based intelligent tutoring system (ABITS).

A conceptual discussion approach is taken.

The agents provide the core reasoning ability. In particular, the paper demonstrates how the system sources and refines a particular set of commonly available data. Also shows how these data are incorporated into the agents' belief set so that they may adapt their behaviour to support individual students.

Provides a framework that can improve learning procedures for future users of ABITS.

This paper provides the specification of a platform for the management of mobile agents, and describes, using Mobile Unified Modeling Language (M‐UML), a mobile game application that uses the specified mobile platform. The specified platform considers the architectural guidelines described by the Foundation for Intelligent Physical Agents that allows the interoperability between agents running on different agent platforms. Our platform specifications provide the application developer with an application programming interface (API) that allows the launching of mobile agents with two optional features, security and fault‐tolerance. Using the platform’s APIs, new mobile games can then be efficiently developed and deployed on the Internet.