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Both criteria score a fitted model as goodness of fit minus a complexity penalty, and lower is better. AIC charges 2 per parameter; BIC charges ln(n) per parameter, so once the sample exceeds about 8 observations BIC penalises complexity harder and tends to select smaller models. AICc adds a small-sample correction and should be preferred when n divided by k is below roughly 40.
Information criteria
AIC = 2k - 2 ln(L); BIC = k ln(n) - 2 ln(L); AICc = AIC + 2k(k+1) / (n - k - 1)
No. The criteria are only comparable between models fitted to exactly the same observations, because the likelihood scale depends on the data.
A gap of 2 to 4 is weak evidence, 4 to 10 is considerable and above 10 is strong evidence against the higher-scoring model.