1999 Fiscal Year Final Research Report Summary
例外を有する一般規則を学習する帰納推論システムの研究
| Project/Area Number |
10680381
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | Kobe University |
|---|
Principal Investigator |
INOUE Katsumi Faculty of Engineering, Kobe University, Associate Professor, 工学部, 助教授 (10252321)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
TOGAWA Kiyoharu Faculty of Engineering, Kobe University, Associate Professor, 工学部, 助手 (50252789)
HANEDA Hiromasa Faculty of Engineering, Kobe University, Professor, 工学部, 教授 (10031113)
|
|---|
| Project Period (FY) |
1998 – 1999
|
| Keywords | Inductive Logic Programming / Extended Logic Programs / Default Rules / Machine Learning / Nonmonotonic Reasoning / Abduction |
|---|
| Research Abstract |
In most previous work on Inductive Logic Programming (ILP), definite Horn programs or classical clausal programs are considered in the form of learned logic programs. However, research on knowledge representation in AI, in particular work on nonmonotonic reasoning, has shown that such monotonic programs are not adequate to represent our commonsense knowledge. To learn default rules or concepts in taxonomic hierarchy, a learning mechanism that deals with nonmonotonic reasoning is necessary. We propose a learning system LELP that learns Extended Logic Programs (ELPs). An ELP allows two kinds of negation, and can represent incomplete knowledge. LELP can learn default rules with exceptions in the form of ELPs, given incomplete positive and negative examples and background knowledge. In LELP, hierarchical defaults can also be learned by recursively calling the exception identification algorithm. Moreover, when some instances are possibly classified as both positive and negative, nondeterministic rules can also be learned.
In this research, we further developed methodologies to learn ELPs. The contributions of the research can be summarized as follows.
1.We proved the correctness of LELP, and analyzed its properties.
2.We implemented various versions of LELP, by applying both top-down and bottom-up algorithms for producing general rules. To speed up the system, we also reimplemented LELP in Java, applied Genetic Algorithm to LELP, and automatically generated the search bias.
3.We considered an extension of LELP which can learn not only ELPs but also Abductive Logic Programs (ALPs). The new technique to discover new abducibles in ALPs can also be applied to a method to learn preference knowledge in nonmonotonic reasoning.
|
Research Products
(14 results)
