Recent work has also been directed toward learning structural models and numeric parameters from data from existing data or knowledge bases, statistical databases, or the agent’s own experience in diagnosis or problem solving.
3.4.3 Research Opportunities
Four basic challenges face current research in plausible inference; two concern development of new methods and two concern modelling new kinds of information.
First, methods must be devised for automatic construction of network structures from fragmentary input, especially input involving a combination of formal specifications, natural language databases, raw statistical data, or existing databases or knowledge sources. In addition to the current emphasis on building a monolithic model that is then applied to many situations, some applications require that a model be built on demand to solve a new and novel problem. Second, abstraction and approximation methods are needed for handling very large network structures. Promising avenues include sampling techniques, methods for pruning scenarios of low likelihood or low relevance, and exploitation of structural regularities in large databases. The third challenge is developing models of user preferences. To build high-quality solutions to problems, an agent must have a good definition of quality that takes into account a user’s preferences. Representations must be developed that simultaneously are rich, are easily elicited, and can be used to solve the problem effectively. Finally, the capabilities of current systems must be expanded to encompass the capability of communicating with, and reasoning about, mental attitudes, including the development of semantics and algorithms for introspective reasoning (reasoning about the agent’s own mental states) and social reasoning (reasoning about the mental states of other agents).
3.5 Agent Architecture
Agents, as we defined previously, are entities capable of autonomous goal-oriented behavior in some environment, often in the service of larger-scale goals external to themselves. The architecture of an agent is the computational structure that, along with the more dynamic knowledge represented within it, generates the agent’s behavior in the environment. The architecture must contain structures that enable representing knowledge (Subsection 3.1), representing and achieving goals (Subsection 3.3), interacting with the environment, and coping with unexpected occurrences. Moreover, for many domains, these capabilities must be exhibited in real time. Depending on the nature of the environment, other agents (either human or virtual) in the environment, and the kinds of task the agent should perform in the environment, other capabilities may also need to be supported in the agent’s architecture; for example, coordination and collaboration (Subsection 3.6), language use (Subsection 3.8), learning (Subsection 3.2), and humanlike behavior and affect.
3.5.1 Relevance to the NII
Agent architectures provide the necessary infrastructure for agents that fill critical roles in both intelligent user interfaces (Subsection 2.1) and software development tools and environments (Subsection 2.3). For example, an intelligent project coach (Subsection 220.127.116.11) is an agent that helps analysts and designers achieve their larger-scale goals in a project environment by recording and explaining choices behind design decisions. The architecture for such an agent needs to provide a basis for representing design knowledge; interacting with the design environment; coping with unexpected occurrences; collaborating with designers; using language (for explanations); and learning about designs, designing, and designers. Similarly, an agent that assists in large-scale group training on the NII by populating a virtual-reality environment that includes other agents (Subsection 2.1.4), such as collaborators, competitors, assistants, leaders and instructors, needs to be built on an architecture that provides most of the capabilities listed previously (and quite possibly more).
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