The Role of Intelligent Systems in the National Information Infrastructure, страница 23

Yellow-pages and consumer reports agents: Creating, finding, and providing new information services, and helping people find the information they seek present major problems for the NII. Capabilities for constructing information brokers that constantly monitor a single, relatively narrow area of interest can help address these problems. Each broker would construct an extensive representation of the content of relevant information sources, the capabilities of service providers, and the scope of related brokers.

3.6.2 State of the Art

The field of multiagent systems has integrated ideas from economics and linguistics with those of computer science. Game theory provides a solid mathematical foundation for studying collaboration and negotiation algorithms, but game theory by itself is insufficient because it does not provide algorithms for computing optimal strategies or determining equilibrium courses of behavior. AI research uses game-theoretic concepts as a guide in the design and analysis of practical agent collaboration and negotiation algorithms. Achievements include the following: identification of protocols (global constraints on messages between the agents) that lead to quick agreements and reduce the incentive for one agent to try and deceive another; design of interagent communication paradigms and languages (for example, formalizations of speech-act theory, KQML, and AOP), for making interagent requests and coming to consensus about the mental states of other agents; and successful implementation of multiagent systems in a variety of application domains (for example, transportation planning, distributed resource allocation, telephone network management, sensor interpretation, manufacturing, and factory automation).

3.6.3 Research Opportunities

The NII presents a variety of challenges for AI research in collaboration and coordination algorithms. The vast number of communicating intelligent agents will challenge the scalability of current theories and methods. Agents must be able to efficiently select the most knowledgeable set of partners with whom to coordinate in service of a task or information-gathering objective. As a result, agents need means for advertising their existence, interests and services; narrow casting methods will be a crucial component in insuring that agents do not get swamped with irrelevant junk messages.

Because different intelligent agents can have different mandates, flexible incentive structures will be necessary to assure cooperation. Negotiation will be crucial for resolving conflicts in goals, information and results, and negotiation algorithms must take into consideration tradeoffs between the time spent searching for appropriate agents and information sources, the time to access a given service, and the information quality and timeliness of information delivery. Techniques developed for negotiation must be extended to deal with situations in which people as well as computer agents participate in the collaborative or coordinated activity.

Research on theories of collaboration and coordination among multiple agents provides insight into these issues and tradeoffs as they occur for a relatively small number of nearly homogeneous agents. The NII provides a test-bed environment for scaling up and refining these theories to deal with very large and heterogeneous communities, in which the set of agents changes dynamically; new services appear; and the underlying languages, protocols, and ontologies evolve over time.

3.7 Ontological Development

The goal of research in ontologies is to create explicit, formal catalogs of knowledge that can be used by intelligent systems. An ontology is a theory of a particular domain or sphere of knowledge, describing the kinds of entity involved in it and the relationships that can hold among different entities. An ontology for finance, for example, would provide working definitions of concepts like money, banks, and stocks. This knowledge is expressed in computer-usable formalisms; for example, an agent for personal finances would draw on its finance ontology, as well as knowledge of your particular circumstances, to look for appropriate investments. Ontologies are broad , in that they cover a wide range of phenomena and situations. They are multi-purpose in that the same ontology can be used in different programs to accomplish a variety of tasks.