Exploiting Relevance through Model-Based Reasoning
Roni Khardon, Dan Roth · 1994
Introduction Since omnipotent reasoning is hard to perform it is natural to look for shortcuts that (sometime) perform well. We say that some data is relevant to a task if it supports an efficient computation that performs correctly on the task. We explore a few aspects of relevance and show that model-based reasoning can support these representations and tasks. (1)Reasoning within context is a natural way to use only the information relevant to the situation when arriving to conclusions. We present two approaches to reasoning in which "context" information, when incorporated with model-based reasoning, makes the computational problems easier. Using these techniques an intelligent agent can construct its view of the world incrementally by pasting together many "narrower" views from different contexts. (2) In some cases, where the task is relatively simple and the environment is very complex, modeling the world exactly would overload an intellige