What is the difference between monotonicity and the admissibility of a heuristic?

Question

I\'m reading over my AI textbook and I\'m curious about what the difference is between monotonicity and admissibility of heuristics (I know they aren\'t mutually exclusive).

Answer 1

Monotonic learning is when an agent may not learn any knowledge that contradicts what it already knows. For example, it may not replace a statement with its negation. Thus, the knowledge base may only grow with new facts in a monotonic fashion. The advantages of monotonic learning are:

1.greatly simplified truth-maintenance

2.greater choice in learning strategies

Non-monotonic learning is when an agent may learn knowledge that contradicts what it already knows. So it may replace old knowledge with new if it believes there is sufficient reason to do so. The advantages of non-monotonic learning are:

1.increased applicability to real domains,

2.greater freedom in the order things are learned in

A related property is the consistency of the knowledge. If an architecture must maintain a consistent knowledge base then any learning strategy it uses must be monotonic.