Defining new markets for intelligent agents

Most agent applications are fairly straightforward: access a Web site, fetch material; in short, perform a simple fixed mission. Others do more personalized tasks such as filtering e-mail or updating legacy systems. From a programming viewpoint, agents are simply active objects that have been defined to simulate parts of a model. Agent based modeling and simulation then become a natural extension of the object oriented paradigm. Simulations of events that involve these kinds of agents (known as actors or demons) have assisted human decision making for decades in batch manufacturing, transportation, and logistics, for example. But work in complex adaptive systems (CAS) may be defining a new kind of agent: one that can actually evolve over time in response to its environment. The beginnings of these adaptive systems are already evident in more advanced agents, which can do simple negotiations on a user's behalf to secure goods and services in an auction, for example. The challenge now is to see how agents bargain and learn in a more complex environment. The Electric Power Research Institute (EPRI), for example, has funded research into agent based auctioning as a way to address the fierce competition for resources. As electric power marketers become available, wholesale electric customers are learning to shop around for the best suppliers. Like agents that represent individual human users, the agents bargaining on behalf of these suppliers and wholesalers decide things like how much to buy, which agent to buy from, how much to pay, and how to manage the exchange of power and money.