Explicitly Represented Knowledge Base
An Explicitly Represented Knowledge Base is a Knowledge Base with Explicit Rules.
References
2015
- (Russell, 2015) ⇒ Stuart Russell. (2015). “Unifying Logic and Probability.” In: Communications of the ACM Journal, 58(7). doi:10.1145/2699411
- QUOTE: Perhaps the most enduring idea from the early days of AI is that of a declarative system reasoning over explicitly represented knowledge with a general inference engine. Such systems require a formal language to describe the real world; and the real world has things in it. For this reason, classical AI adopted first-order logic — the mathematics of objects and relations — as its foundation.
The key benefit of first-order logic is its expressive power, which leads to concise — and hence learnable — models. For example, the rules of chess occupy 100 pages in first-order logic, 105 pages in propositional logic, and 1038 pages in the language of finite automata. The power comes from separating predicates from their arguments and quantifying over those arguments: so one can write rules about On (p, c, x, y, t) (piece p of color c is on square x, y at move t) without filling in each specific value for c, p, x, y, and t.
- QUOTE: Perhaps the most enduring idea from the early days of AI is that of a declarative system reasoning over explicitly represented knowledge with a general inference engine. Such systems require a formal language to describe the real world; and the real world has things in it. For this reason, classical AI adopted first-order logic — the mathematics of objects and relations — as its foundation.