Matt's AGI <http://mattmahoney.net/agi2.html> uses an approximates conditional Kolmogorov Complexity:
K(Y|X) with conditional compression : C(Y|X) = C(XY) - C(X) C compresses and provides the resulting length. Juxtaposition concatenates, ie "XY" is concatenate(X,Y). He then uses this to route messages to the most intelligent agent for that message via a measure semantic distance between strings X and Y: D(X,Y) =C(Y|X) + C(X|Y) If one has the less ambitious goal of scoring relative causation of substrings of the same corpus, one needs a different measure. Let's say you have a corpus string Z and two of its substrings X and Y. PROPOSITION: We can say X is more causative in Z than is Y if: CausalScore(Z|X) < CausalScore(Z|Y) WHERE CausalScore(A|B) = C(A-B)/Size(A-B) "-" deletes the right hand string out of the left hand string. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T9a8649791989a23a-M0b75909812e0a928987f48ea Delivery options: https://agi.topicbox.com/groups/agi/subscription
