Knowledge Representation Issues

(1st-August-2020)

• Here are knowledge representation issues.

1. Background Knowledge and Observations

2. Querying the User

3. Knowledge-Level Explanation

4. How Did the System Prove a Goal?

5. Why Did the System Ask a Question?

6. Knowledge-Level Debugging

7. Incorrect Answers

8. Missing Answers

9. Infinite Loops

• An observation is a piece of information received online from users, sensors, or other knowledge sources. For this chapter, we assume an observation is an atomic proposition. Observations are implicitly conjoined, so a set of observations is a conjunction of atoms. Neither users nor sensors provide rules directly from observing the world. The background knowledge allows the agent to do something useful with these observations.

• In many reasoning frameworks, the observations are added to the background knowledge. But in other reasoning frameworks (e.g, in abduction, probabilistic reasoning, and learning), observations are treated separately from background knowledge. Users cannot be expected to tell us everything that is true. First, they do not know what is relevant, and second, they do not know what vocabulary to use. An ontology that specifies the meaning of the symbols, and a graphical user interface to allow the user to click on what is true, may help to solve the vocabulary problem. However, many problems are too big; what is relevant depends on other things that are true, and there are too many possibly relevant truths to expect the user to specify everything that is true, even with a sophisticated graphical user interface.

• Similarly, passive sensors are able to provide direct observations of conjunctions of atomic propositions, but active sensors may have to be queried by the agent for the information that is necessary for a task.