The key problem in agent architecture is finding a compatible mix of the necessary capabilities and integrating them together to support appropriate, most likely real-time, behavior. Compatibility is key here. True intelligent behavior requires such a close integration of these capabilities that arbitrary combinations of them are as likely to degrade performance as enhance it. For example, a main result from research on the combination of learning and planning is that the acquisition of rules intended to improve the speed of the planner usually has the inverse effect unless the combination is done just right.
3.5.2 State of the Art
The field of agent architecture is just starting to reach maturity. A rich body of work is now available on the individual capabilities, along with real-time versions of several of them. Building on this base, dozens of proposals have recently been generated about how to combine small subsets of the total set of capabilities (although mostly not in real time). A small number of systems have demonstrated the ability to exhibit a handful or more of the key capabilities in real time. For example, one such system has yielded automated intelligent pilots that have successfully participated in an operational military-training exercise.
3.5.3 Research Opportunities
An effective and efficient integration of all the key capabilities is still a long-term project. However, many high-value applications only require subsets of these capabilities; for example, an intelligent project coach might have only weak real-time requirements and no requirement to behave in a humanlike manner. Applications might also be able to get by with approximate versions of other capabilities and still perform useful functions. What is therefore needed now--and what the field is clearly ready to provide--are significant pushes to develop both real-time versions of a wider set of the key capabilities and larger and more varied combinations of capabilities (even if not in real time). The investigation of incremental approaches to integration--in which initially small subsets of prototype capabilities are combined and applied, and then both the number of capabilities and the quality of the resulting behavior are gradually improved and applied in a wider range of domains--is one promising means of addressing this latter need.
3.6 Multiagent Coordination and Collaboration
The field of multiagent coordination has studied the problem of endowing agents with the ability to communicate with each other to reach mutually beneficial agreements. Specialized techniques have been developed to enable an agent to represent and reason about the capabilities of other agents. Research on collaboration has led to representations of the information agents must establish and specifications of the information agents must communicate in order to collaborate. Research has also led to algorithms that enable two agents to communicate their differing objectives, determine areas of shared interest, and converge on Pareto-optimal agreements that increase the utility of all participants.
3.6.1 Relevance to the NII
In Section 2, we described intelligent agents that act as personal assistants and software brokers that support information retrieval and other advanced services. These possibilities suggest that the majority of network interactions will eventually be between programs. To make these interactions flexible, intelligent coordination and collaboration between agents will be essential. Furthermore, collaborative capabilities could significantly enhance human-computer interfaces. A sampling of tasks such agents could perform illustrates the promise and challenge of multiagent coordination and collaboration.
Payment and delivery of services: As electronic commerce blossoms on the NII, organizations and individuals will require assistance in finding the most attractive product or service among potentially hundreds of such products and services advertised on the NII. Personalized and trusted bargaining agents could act on behalf of their users by first finding information and then negotiating with selling agents over price, conditions of payment, and delivery schedules.
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