Prioritizing Multiple, Contradictory Sources in Common-Sense Learning by Being Told; or, Advice-Taker Meets Bureaucracy

An important common-sense capability is to learn by taking advice, or being told, from a variety of sources of information: messages from varous other agents, reading various texts, etc.. Such learned or assimilated knowledge provides an important basis for one's actions. Yet a basic feature of life is that one cannot believe everything that one is told. Not only may advice be incorrect; e.g., it may be contradicted by direct experience. Worse, different sources of advice information may contradict each other, or even thermselves. How is an advice-taking agent to maintain a consistent, yet usefully actionable, set of beliefs, then? Here, we address this problem, which has not received much previous attention in the AI literature. We illustrate, with an actual example, how it arises in the domain of bureaucracy. A common human strategy is to treat advice as working belief, which can be overridden by other advice and direct experience. This suggests the first element of our approach: to represent advice as default-status knowledge.

By: Benjamin N. Grosof

Published in: RC20124 in 1995


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