Abstract
A class of Probabilistic Abductive Logic Programs (PALPs) is introduced
and an implementation is developed in CHR for solving
abductive problems, providing minimal explanations with their
probabilities.
Both all-explanations and most-probable-explanations versions are given.
Compared with other probabilistic versions of
abductive logic programming, the approach is characterized by
higher generality and a flexible and adaptable
architecture which incorporates integrity constraints and interaction
with external constraint solvers.
A PALP is transformed in a systematic way into a CHR program which serves
as a query interpreter, and the resulting CHR code describes in a highly
concise way, the strategies applied in the search for explanations.
Udgivelsesdato: December 2008
and an implementation is developed in CHR for solving
abductive problems, providing minimal explanations with their
probabilities.
Both all-explanations and most-probable-explanations versions are given.
Compared with other probabilistic versions of
abductive logic programming, the approach is characterized by
higher generality and a flexible and adaptable
architecture which incorporates integrity constraints and interaction
with external constraint solvers.
A PALP is transformed in a systematic way into a CHR program which serves
as a query interpreter, and the resulting CHR code describes in a highly
concise way, the strategies applied in the search for explanations.
Udgivelsesdato: December 2008
| Originalsprog | Engelsk |
|---|---|
| Bogserie | Lecture Notes in Computer Science |
| Vol/bind | 5388 |
| Sider (fra-til) | 85-118 |
| ISSN | 0302-9743 |
| Status | Udgivet - 2008 |
Bibliografisk note
Undertitel på bind: "Constraint Handling Rules, Current Research Topics"Schrijvers, Tom; Frühwirth, Thom (Eds.)
ISBN: 978-3-540-92242-1