Modern planning theory is beginning to understand the issues involved in reasoning with incomplete information, and coping with costs and time pressure. The first working systems to realize existing theories are now being tested on real applications. The NII will provide an ideal framework in which to evaluate and refine these theories. The success of these prototype systems will be measured by the increased efficiency of information workers who can spend less time searching for and through information.
3.4 Plausible Reasoning
The AI field of plausible reasoning has tackled the problem of representing, understanding, and controlling the behavior of agents or other systems in the context of incomplete or incorrect information. This research has led to a number of techniques, algorithms, and implemented systems for describing, diagnosing, and manipulating both natural and manmade artifacts. By basing these techniques on the sound foundation of probability theory, AI researchers have been able to assemble large knowledge bases in a principled way; effectively perform both predictive and diagnostic reasoning about the system; and develop control policies or plans that could enable the system to act safely, effectively, and efficiently.
At least four themes underlie this work: (1) making the structure of the target system explicit; (2) representing uncertainty about the system coherently and explicitly; (3) updating beliefs about the system as new information about it is received; and (4) reasoning about tradeoffs or relative likelihoods in the prediction, diagnosis, or decision task. A hallmark of this approach has been the development of models that combine both structural and numeric components. The structural (or symbolic) component indicates dependencies or independencies among system components; the numeric component quantifies the extent of the dependency, the strength of belief in a relationship, or the relative likelihood of various results.
3.4.1 Relevance to the NII
Uncertainty, incomplete or incorrect information, and the need to generate high-quality behavior or make high-quality predictions or diagnoses will be central to NII applications. An intelligent agent cannot possibly have complete and timely information about Internet, diagnosing problems with human or complex nonhuman systems is inexact and characterized by noisy and conflicting information, and manufacturing and logistical control problems are fraught with uncertainty. In all cases, there is a need for developing cost-effective solutions, which requires reasoning about tradeoffs between the cost and likelihood of success, the relative quality of alternative courses of action, and the value of obtaining more information versus the cost of doing so.
3.4.2 State of the Art
Most of the historical success of probabilistic or decision-theoretic models has been expert applications. In these applications, a model (or at least the structural component of the model) for a domain is elicited from a human expert and is then used to solve a wide range of problems from the domain. These systems have achieved a high level of effectiveness, size, speed, and accuracy for tasks such as diagnosing diseases and suggesting treatments in human medical care as well as troubleshooting problems with complex devices such as aircraft. This success is in large part owed to the fact that the framework provides a natural way to capture crucial knowledge about the system: structural regularities, degree of uncertainty, value of information, preferences or utilities, and relative likelihood.
Research has also addressed the planning or decision-making problem, generating courses of action that solve a problem or carry out a task effectively. This work has been applied both to aiding human decision makers in constructing and solving domain models and to importing representations and algorithms from classical AI, control, and stochastic optimization to build and solve these models automatically. Work in decision making has also been extended to the concept of meta-rationality; in planning an effective course of action the agent must take into account not only the cost of taking action but also the cost of delaying action.
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