𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Support set selection for a bductive and default reasoning

✍ Scribed by Bart Selman; Hector J. Levesque


Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
987 KB
Volume
82
Category
Article
ISSN
0004-3702

No coin nor oath required. For personal study only.

✦ Synopsis


Of all the possible ways of computing abductive explanations, the ATMS procedure is one of the most popular. While this procedure is known to run in exponential time in the worst case, the proof actually depends on the existence of queries with an exponential number of answers. But how much of the difficulty stems from having to return these large sets of explanations? Here we explore abduction tasks similar to that of the AIMS, but which return relatively small answers. The main result is that although it is possible to generate some nontrivial explanations quickly, deciding if there is an explanation containing a given hypothesis is NP-complete, as is the task of generating even one explanation expressed in terms of a given set of assumption letters. Thus, the method of simply listing all explanations, as employed by the ATMS, probably cannot be improved upon. An interesting result of our analysis is the discovery of a subtask, we call the Support Set Selection Task, that is not only at the core of generating explanations, but is also at the core of generating extensions in Reiter's default logic. Moreover, it is this subtask that accounts for the computational difficulty of both forms of reasoning. This establishes for the first time a strong connection between computing abductive explanations and computing extensions in default logic.


πŸ“œ SIMILAR VOLUMES


Variable selection and oversampling in t
✍ Wolfgang HΓ€rdle; Yuh-Jye Lee; Dorothea SchΓ€fer; Yi-Ren Yeh πŸ“‚ Article πŸ“… 2009 πŸ› John Wiley and Sons 🌐 English βš– 291 KB πŸ‘ 1 views

## Abstract In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objectives regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of

Empirical support for a basic TAT set
✍ Floyd S. Irvin; Kenneth Vander Woude πŸ“‚ Article πŸ“… 1971 πŸ› John Wiley and Sons 🌐 English βš– 203 KB

Uncovered sun), using those 107 of a total of 151 suns that could be scored unambiguously. The resulting x2 value was significant far beyond the p 5 .001 level ( x2 = 55.29, df = 1). Covered suns were not more frequent in the drawings of boys (Boys: Girls by Covered Sun: Uncovered Sun); the x2 valu

Selecting Relevant Descriptors for Class
✍ Miriam Carbon-Mangels; Michael C. Hutter πŸ“‚ Article πŸ“… 2011 πŸ› Wiley (John Wiley & Sons) 🌐 English βš– 499 KB

## Abstract Classification algorithms suffer from the curse of dimensionality, which leads to overfitting, particularly if the problem is over‐determined. Therefore it is of particular interest to identify the most relevant descriptors to reduce the complexity. We applied Bayesian estimates to mode