Abstract
Agent-oriented software engineering has, by many researchers, been dubbed the new paradigm in software development, and from its original concepts in the early ‘80s, agents and agent systems are now active research areas in computer science. This evolution offers a promising approach to the development of patient scheduling systems. Coordinating and processing a vast amount of complex variables, such a system should be designed to stock and schedule a wide range of resources based on the patients’ health condition and availability, drawing on the advantageous data control and optimization abilities of agent technologies. This article presents the design of a working agentbased patient scheduling system prototype.
Key Terms in this Chapter
Situatedness: Agents are in close relationship to its environment. Agents optimally have awareness capabilities, supervising their domain through sensors and performing reactive actions through effectors accordingly (Jennings & Wooldridge, 2000; Weyns, Steegmans, & Holvoet, 2004).
Negotiation: Often used amongst agents as a means of optimal decision making. Agents can exhibit the ability to perform an optimal individual action, which contributes to the goals of the system as a whole.
Agent System: “A group of agents that can potentially interact with each other” (Vlassis, 2003). “In multi agent systems, applications are designed and developed in terms of autonomous software entities (agents) that can flexibly achieve their objectives by interacting with one another in terms of high-level protocols and languages” (Zambonelli, Jennings, & Wooldridge, 2003).
Mobility: The ease of transferring agents to other systems with the same characteristics, due to their autonomous nature and the dynamic property of agent systems.
Social Ability: Agents communicate and reach mutual decisions through conversation and negotiation (Foner, 1993; Wooldrige & Ciancarini, 2001).
Dynamic Environment: Agent systems allow agents to enter, leave and change at runtime.
Agent: “A computer system that is capable of flexible autonomous action in dynamic, unpredictable, typically multi-agent domains” (Luck et al., 2005).
Anthropomorphism: The attribution of human characteristics to software agents, including personalizability, learning and reasoning (Foner, 1993).