Abstract
An intelligent system is a system that has, similar to a living organism, a coherent set of components and subsystems working together to engage in goal-driven activities. In general, an intelligent system is able to sense and respond to the changing environment; gather and store information in its memory; learn from earlier experiences; adapt its behaviors to meet new challenges; and achieve its pre-determined or evolving objectives. The system may start with a set of predefined stimulusresponse rules. Those rules may be revised and improved through learning. Anytime the system encounters a situation, it evaluates and selects the most appropriate rules from its memory to act upon. Most human organizations such as nations, governments, universities, and business firms, can be considered as intelligent systems. In recent years, researchers have developed frameworks for building organizations around intelligence, as opposed to traditional approaches that focus on products, processes, or functions (e.g., Liang, 2002; Gupta and Sharma, 2004). Today’s organizations must go beyond traditional goals of efficiency and effectiveness; they need to have organizational intelligence in order to adapt and survive in a continuously changing environment (Liebowitz, 1999). The intelligent behaviors of those organizations include monitoring of operations, listening and responding to stakeholders, watching the markets, gathering and analyzing data, creating and disseminating knowledge, learning, and effective decision making. Modeling intelligent systems has been a challenge for researchers. Intelligent systems, in particular, those involve multiple intelligent players, are complex systems where system dynamics does not follow clearly defined rules. Traditional system dynamics approaches or statistical modeling approaches rely on rather restrictive assumptions such as homogeneity of individuals in the system. Many complex systems have components or units which are also complex systems. This fact has significantly increased the difficulty of modeling intelligent systems. Agent-based modeling of complex systems such as ecological systems, stock market, and disaster recovery has recently garnered significant research interest from a wide spectrum of fields from politics, economics, sociology, mathematics, computer science, management, to information systems. Agent-based modeling is well suited for intelligent systems research as it offers a platform to study systems behavior based on individual actions and interactions. In the following, we present the concepts and illustrate how intelligent agents can be used in modeling intelligent systems. We start with basic concepts of intelligent agents. Then we define agent-based modeling (ABM) and discuss strengths and weaknesses of ABM. The next section applies ABM to intelligent system modeling. We use an example of technology diffusion for illustration. Research issues and directions are discussed next, followed by conclusions.
TopIntelligent Agent
Intelligent agents, also known as software agents, are computer applications that autonomously sense and respond to environment in the pursuit of certain designed objectives (Wooldridge and Jennings, 1995). Intelligent agents exhibit some level of intelligence. They can be used to assist the user in performing non-repetitive tasks, such as seeking information, shopping, scheduling, monitoring, control, negotiation, and bargaining.
Intelligent agents may come in various shapes and forms such as knowbots, softbots, taskbots, personal agents, shopbots, information agents, etc. No matter what shape or form they have, intelligent agents exhibit one or more of the following characteristics:
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Autonomous: Being able to exercise control over their own actions.
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Adaptive/Learning: Being able to learn and adapt to their external environment.
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Social: Being able to communicate, bargain, collaborate, and compete with other agents on behalf of their masters (users).
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Mobile: Being able to migrate themselves from one machine/system to another in a network, such as the Web.
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Goal-oriented: Being able to act in accordance with built-in goals and objectives.
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Communicative: Being able to communicate with people or other agents thought protocols such as agent communication language (ACL).
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Intelligent: Being able to exhibit intelligent behavior such as reasoning, generalizing, learning, dealing with uncertainty, using heuristics, and natural language processing.
Key Terms in this Chapter
Intelligent Agent: An autonomous software program that is able to learn and adapt to its environment in order to perform certain tasks delegated to it by its master.
Diffusion of Innovation: Popularized by Everett Rogers, it is the study of the process by which an innovation is communicated and adopted over time among the members of a social system.
Agent Based Modeling: Using intelligent agents and their actions and interactions in a given environment to simulate the complex dynamics of a system.
Intelligent System: A system that has a coherent set of components and subsystems working together to engage in goal-driven activities.
Intelligent System Modeling: The process of construction, calibration, and validation of models of intelligent systems.
Organizational Intelligence: The ability of an organization to perceive, interpret, and select the most appropriate response to the environment in order to advance its goals.
Multi-Agent System: A distributed system with a group of intelligent agents that communicate, bargain, compete, and cooperate with other agents and the environment to achieve goals designated by their masters.