Inductive logic programming algorithm for estimating quality of partial plans
Nowaczyk, Slawomir LU and Malec, Jacek LU
(2007)
6th Mexican International Conference on Artificial Intelligence (MICAI 2007)
4827.
p.359-369
- Abstract
- We study agents situated in partially observable environments, who do not have the resources to create conformant plans. Instead, they create conditional plans which are partial, and learn from experience to choose the best of them for execution. Our agent employs an incomplete symbolic deduction system based on Active Logic and Situation Calculus for reasoning about actions and their consequences. An Inductive Logic Programming algorithm generalises observations and deduced knowledge in order to choose the best plan for execution. We show results of using PROGOL learning algorithm to distinguish "bad" plans, and we present three modifications which make the algorithm fit this class of problems better. Specifically, we limit the search... (More)
- We study agents situated in partially observable environments, who do not have the resources to create conformant plans. Instead, they create conditional plans which are partial, and learn from experience to choose the best of them for execution. Our agent employs an incomplete symbolic deduction system based on Active Logic and Situation Calculus for reasoning about actions and their consequences. An Inductive Logic Programming algorithm generalises observations and deduced knowledge in order to choose the best plan for execution. We show results of using PROGOL learning algorithm to distinguish "bad" plans, and we present three modifications which make the algorithm fit this class of problems better. Specifically, we limit the search space by fixing semantics of conditional branches within plans, we guide the search by specifying relative relevance of portions of knowledge base, and we integrate learning algorithm into the agent architecture by allowing it to directly access the agent's knowledge encoded in Active Logic. We report on experiments which show that those extensions lead to significantly better learning results. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1409729
- author
- Nowaczyk, Slawomir
LU
and Malec, Jacek
LU
- organization
- publishing date
- 2007
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- MICAI 2007: Advances in Artificial Intelligence / Lecture notes in computer science
- volume
- 4827
- pages
- 359 - 369
- publisher
- Springer
- conference name
- 6th Mexican International Conference on Artificial Intelligence (MICAI 2007)
- conference location
- Aguascalientes, Mexico
- conference dates
- 2007-11-04 - 2007-11-10
- external identifiers
-
- wos:000251037900034
- scopus:38149099905
- ISSN
- 0302-9743
- 1611-3349
- ISBN
- 978-3-540-76630-8
- DOI
- 10.1007/978-3-540-76631-5_34
- language
- English
- LU publication?
- yes
- id
- ee954edc-1ff6-4f86-9c84-458cdb837f3d (old id 1409729)
- date added to LUP
- 2016-04-01 12:21:02
- date last changed
- 2024-01-08 17:28:39
@inproceedings{ee954edc-1ff6-4f86-9c84-458cdb837f3d,
abstract = {{We study agents situated in partially observable environments, who do not have the resources to create conformant plans. Instead, they create conditional plans which are partial, and learn from experience to choose the best of them for execution. Our agent employs an incomplete symbolic deduction system based on Active Logic and Situation Calculus for reasoning about actions and their consequences. An Inductive Logic Programming algorithm generalises observations and deduced knowledge in order to choose the best plan for execution. We show results of using PROGOL learning algorithm to distinguish "bad" plans, and we present three modifications which make the algorithm fit this class of problems better. Specifically, we limit the search space by fixing semantics of conditional branches within plans, we guide the search by specifying relative relevance of portions of knowledge base, and we integrate learning algorithm into the agent architecture by allowing it to directly access the agent's knowledge encoded in Active Logic. We report on experiments which show that those extensions lead to significantly better learning results.}},
author = {{Nowaczyk, Slawomir and Malec, Jacek}},
booktitle = {{MICAI 2007: Advances in Artificial Intelligence / Lecture notes in computer science}},
isbn = {{978-3-540-76630-8}},
issn = {{0302-9743}},
language = {{eng}},
pages = {{359--369}},
publisher = {{Springer}},
title = {{Inductive logic programming algorithm for estimating quality of partial plans}},
url = {{http://dx.doi.org/10.1007/978-3-540-76631-5_34}},
doi = {{10.1007/978-3-540-76631-5_34}},
volume = {{4827}},
year = {{2007}},
}