Machine Learning methods in symbolic form:
Bay: @s1 -> @s2
Gen: <{@s_k_1,...,@s_k_n}>
So incrementally try @s_k for outcome @s2
Hypothesis testing
Imagination
Creativity
Ana: {@s1,{v1,@s2}} -> {@s1,{v2,@s3}}
This like this
Con: {@s1,@s2} -> @s3
Categorization
Sym: {<@s1>,{is,@s2}},{@s2,{is,@s3}}
-> {@s1,{is,@s3}}
From history of s1 and s2, and current s1, predict s2
Synthesizer across time
Historic s1,s2 -> s2
So, s1 -> s2
