Prospectus, February 16, 1978

STU-GO VACANCIES FILLED TODAY, TOMORROW; Trail is unopposed for presidential post; Ballje, Berry, Henze vie for veep\u27s job; Staff editorial: Should Parkland foot the bill for vets\u27 education?; Parkland College News in Brief: CHI helps you understand the doctor, SWAMP meets, Land lab has good season, SIU rep here today; Return of the Hilltoppers: Clambering up Mount Parkland -- \u27Because it was there!\u27; Treasury post draws two hopefuls; One running for secretary; Davis seeks PR position; Bundy unopposed in bid for student services post; Three candidates in race for convocations senator; Swanson pursuing office of day senator; Life spanning draws large crowd; Snow day melts extra study time; Will the big bands ever return?; Taped artist interviews at U of I; Toll free tax answers for Illinois residents; \u27Furry friends\u27 contest; Classifieds; State basketball tourney schedule; Women win 10th: Cobras take victory number 20; Long life program lists classes; Cherry Orchard opening is apple of Krannert\u27s eye; Bookworms invited to U of I; It\u27s tourney time; Women beat Kankakee, top .500; Bouncing Bob Basketball Bonanza: If you think LAST week was tough...; Bouncing Bob Basketball Bonanza; Men grab two more winshttps://spark.parkland.edu/prospectus_1978/1025/thumbnail.jp

Recovering incidence functions

In incidence calculus, inferences are usually made by calculating incidence sets and computing probabilities of formulae based on a given incidence function in an incidence calculus theory. Incidence functions are vital for performing any further inference. Without the existence of this function, many of the features of incidence calculus will be lost. However it is still the case that numerical values are assigned on some formulae directly without giving the incidence function. This paper discusses how to recover incidence functions in these cases. The result can be used to calculate mass functions from belief functions in the Dempster-Shafer theory of evidence (or DS theory) and define probability spaces from inner measures (or lower bounds) of probabilities on the relevant propositional language set. 1 Introduction Incidence calculus [1, 3] as an alternative approach to dealing with uncertainty has a special feature i.e., the indirect association of numerical uncertain assignment o..

On the relations between incidence calculus and ATMS

Abstract. This paper discusses the relationship between incidence calculus and the ATMS. It shows that managing labels for statements in an ATMS is similar to producing the incidence sets of these statements in incidence calculus. We willprove that a probabilistic ATMS can be implemented using incidence calculus. In this way, we can not only produce labels for all nodes in the system automatically, but also calculate the probability ofanyofsuch nodes in it. The reasoning results in incidence calculus can provide justi cations for an ATMS automatically.

On the Relations between Incidence Calculus and ATMS

. This paper discusses the relationship between incidence calculus and the ATMS. It shows that managing labels for statements in an ATMS is similar to producing the incidence sets of these statements in incidence calculus. We will prove that a probabilistic ATMS can be implemented using incidence calculus. In this way, we can not only produce labels for all nodes in the system automatically, but also calculate the probability of any of such nodes in it. The reasoning results in incidence calculus can provide justifications for an ATMS automatically. 1 Introduction The ATMS is a symbolic reasoning technique used in the artificial intelligence domain to deal with problems by providing dependent relations among statements during inference normally. This technique can only infer results with absolutely true or false. It lacks the ability to draw plausible conclusions such as that a conclusion is true with some degree of belief. However in many cases, pieces of information from a knowledge..