The field of planning (Subsection 3.3) develops algorithms that automatically construct and execute sequences of primitive commands in order to achieve high-level goals. By reasoning about formal models of the capabilities and content of network services and databases, AI planning systems can focus information-gathering activities in profitable directions. Because planning systems take a declarative goal specification as input, they can also help raise the level of user interfaces, allowing users to specify what they want done, then computing actions needed to achieve the goal and determining when these actions should be executed.
Work in plausible reasoning (Subsection 3.4) has leveraged statistical principles to devise principled encodings for many forms of uncertain information. Algorithms have been developed to support diagnostic reasoning, causal inference, and evaluation of the tradeoffs between plan cost and goal satisfaction. Plausible reasoning techniques are especially appropriate for National Challenge application areas such as health care, but are applicable to the information infrastructure as well. For example, intelligent help systems can use behavior traces to assemble probabilistic profiles of user goals, and personal assistants might assess tradeoffs between the user’s conflicting objectives.
The study of agent architecture (Subsection 3.5) seeks to integrate specialized AI subfields to create intelligent agents, robust entities that are capable of autonomous, real-time behavior in an environment over an extended period of interaction. Agent architectures could provide the integration needed to support a variety of critical roles in the NII, including personal assistants; intelligent project coaches; and large-scale, distributed, group-trainers.
Research into multiagent coordination and collaboration (Subsection 3.6) has developed techniques for representing the capabilities of other agents and has specified the knowledge needed by agents to collaborate. Negotiation algorithms have been developed that allow two intelligent agents to determine areas of shared interest and compute agreements that increase the utility of all participants. This area is crucial to the NII because the sheer scope of the infrastructure will demand that much activity be performed by software agents, without detailed supervision by people. Techniques developed in this area will also play central roles in developing more collaborative and flexible systems for human-computer communication.
The goal of ontological development (Section 3.7) is to create explicit, formal, multipurpose catalogs of knowledge that can be used by intelligent systems. In contrast with knowledge representation research that focuses on the form of representation and methods for reasoning using those forms, research in ontological development focuses on content. An ontology for finance, for example, would provide computer-usable definitions of such concepts as money, banks, and compound interest. Creation of shared systems of vocabulary is crucial to the NII because ontologies provide the conceptualizations and basic knowledge required for communication and collaboration among different agents and between a person and his or her personal intelligent agent.
The fields of speech and language processing (Section 3.8) seek to create systems that communicate with people in natural languages such as written and spoken English. Applications to the NII are vast. Speech systems could revolutionize user interfaces, especially for small, mobile computers. Textual analysis could lead to superior indexing systems and improved information retrieval.
Research in image understanding and synthesis (Subsection 3.9) is leading to algorithms for analyzing photographs, diagrams, and video as well as techniques for the visual display of quantitative and structured information. NII applications for image understanding and synthesis will range from the extraction of semantic content for use in browsing and searching image data to intelligent compression schemes for storage and transmission to enhanced medical imaging to the generation of realistic (or schematic) artificial scenes from models extracted from world images.
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