Representing and Reasoning with Defaults For Learning Agents
Benjamin N. Grosof · 1993
The challenge we address is to create autonomous, inductively learning agents that exploit and mod-ify a knowledge base. Our general approach, em-bodied in a continuing research program (joint with Stuart Russell), is declarative bias, i.e., to use declarative knowledge to constrain the hypothesis space in inductive learning. In previous work, we have shown that many kinds of declarative bias can be relatively efficiently represented and de-rived from background knowledge. We begin by observing that the default, i.e., revisable, flavor of beliefs is crucial in applications, especially for competence to improve incrementally and for in-formation to be acquired through communication, language, and sensory perception in integrated agents. We argue that much of learning in hu-mans consists of "learning in the small " and is nothing more nor less than acquiring new plau-sible premise beliefs. Thus representation of de-faults and plausible knowledge should be a central question for researchers aiming to design sophis-ticated learning agents that exploit a knowledge base. We show that such applications pose sev-eral representational requirements that are unfa-miliar to most in the machine learning community, and whose combination has not been previously addressed by the knowledge representation com-munity. These include: prioritization-type prece-dence between defaults; updating with new de-faults, not just new for-sure beliefs; explicit rea-soning about adoption of defaults and precedence between defaults; and integration of defaults with probabilistic and statistical beliefs. We show how, for the first time, to achieve all of these require-metats, at least partially, in one declarative formal-