BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Sunil Karn (Southern Illinois University) DTSTART:20241205T190000Z DTEND:20241205T200000Z DTSTAMP:20260423T052837Z UID:OLS/161 DESCRIPTION:Title: Be haviorally Correct Language Identification.\nby Sunil Karn (Southern I llinois University) as part of Online logic seminar\n\n\nAbstract\nThe con cept of Behaviorally Correct (BC) language identification\, is a paradigm in inductive inference that allows learners to approximate target language s while tolerating a bounded density of errors. Beginning with foundationa l definitions\, such as those of inductive inference machines (IIMs) and B C identification\, we extend these notions to approximate identification u sing error densities and asymptotic uniform densities. Our results demonst rate the structured inclusion relations between various identification cla sses. Specifically\, we prove that for any r\, r1​∈ [0\,1] with r1​> r\, TxtBCr ​⊂ TxtBCr1\, and similarly UBCr​ ⊂ UBCr1​​ and UTx tBCr ​⊂ UTxtBCr1\, indicating that relaxation of error bounds yields s trictly larger identification classes.\n\nFurthermore\, leveraging the Ope rator Recursion Theorem\, we construct examples demonstrating the non-equi valence of adjacent identification classes\, highlighting the role of part ial recursive functions in these separations. These results emphasize the versatility of BC identification frameworks in accommodating error densiti es while maintaining robust theoretical guarantees. Finally\, we introduce uniform approximate BC identification and establish its utility in addres sing local inconsistencies within language approximation\, culminating in refined criteria that bridge global and local error bounds.\n LOCATION:https://researchseminars.org/talk/OLS/161/ END:VEVENT END:VCALENDAR