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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) In MICAI 2007: Advances in Artificial Intelligence / Lecture notes in computer science 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:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
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)
external identifiers
  • wos:000251037900034
  • scopus:38149099905
ISSN
1611-3349
0302-9743
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
2009-06-01 14:36:43
date last changed
2017-02-19 03:37:41
@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         = {1611-3349},
  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},
  volume       = {4827},
  year         = {2007},
}