, ▁knowledge , ▁and ▁wisdom " ▁( R ug ai , ▁ 2 0 1 3 ) ▁ ▁On ▁making ▁induct ive ▁problems ▁easier ▁to ▁solve : ▁" E lim in ating ▁relevant ▁possibilities , ▁weak ening ▁the ▁convergence ▁criter ion , ▁co ars ening ▁the ▁question , ▁or ▁augment ing ▁the ▁collection ▁of ▁potential ▁strateg ies ▁all ▁tend ▁to ▁make ▁a ▁problem ▁easier ▁to ▁solve " ▁( K elly , ▁ 2 0 0 0 a ) ▁ ▁On ▁the ▁diver gence ▁of ▁computational ▁ep ist em ology ▁from ▁Bay esian ▁confirm ation ▁theory ▁and ▁the ▁like : ▁" When ever ▁you ▁are ▁incl ined ▁to ▁explain ▁a ▁feature ▁of ▁science ▁in ▁terms ▁of ▁probability ▁and ▁confirm ation , ▁take ▁a ▁moment ▁to ▁see ▁how ▁the ▁issue ▁would ▁look ▁in ▁terms ▁of ▁complexity ▁and ▁success " ( K elly , ▁ 2 0 0 0 a ) ▁ ▁Comput ational ▁ep ist em ology ▁in ▁a ▁nut shell : ▁ ▁F ormal ▁learning ▁theory ▁is ▁very ▁simple ▁in ▁outline . ▁An ▁induct ive ▁problem ▁spec ifies ▁a ▁range ▁of ▁ep ist em ically ▁possible ▁world s ▁over ▁which ▁to ▁succeed ▁and ▁determ ines ▁what ▁sort ▁of ▁output ▁would ▁be ▁correct , ▁where ▁correct ness ▁may ▁em body ▁both ▁content ▁and ▁truth ▁( or ▁some ▁analog ous ▁virtue ▁like ▁empir ical ▁ade qu acy ). ▁Each ▁possible ▁world ▁produces ▁an ▁input ▁stream ▁which ▁the ▁induct ive ▁method ▁processes ▁sequ entially , ▁generating ▁its ▁own ▁output ▁stream , ▁which ▁may ▁terminate ▁( ending ▁with