Advanced

An Architecture for Resource Bounded Agents

Nowaczyk, Sławomir and Malec, Jacek LU (2007) International Multiconference on Computer Science and Information Technology In Proceedings of the International Multiconference on Computer Science and Information Technology 2. p.59-69
Abstract
We study agents situated in partially observable environments, who do not have sufficient resources to create conformant (complete) plans. Instead, they create plans which are conditional and partial, execute or simulate them, and learn from experience to evaluate their quality. Our agents employ 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 so that the agents can choose the best plan for execution.

We describe an architecture which allows ideas and solutions from several subfields of Artificial Intelligence to be joined together in a controlled and... (More)
We study agents situated in partially observable environments, who do not have sufficient resources to create conformant (complete) plans. Instead, they create plans which are conditional and partial, execute or simulate them, and learn from experience to evaluate their quality. Our agents employ 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 so that the agents can choose the best plan for execution.

We describe an architecture which allows ideas and solutions from several subfields of Artificial Intelligence to be joined together in a controlled and manageable way. In our opinion, no situated agent can achieve true rationality without using at least logical reasoning and learning. In practice, it is clear that pure logic is not able to cope with all the requirements put on reasoning, thus more domain- specific solutions, like planners, are also necessary. Finally, any realistic agent needs a reactive module to meet demands of dynamic environments.

Our architecture is designed in such a way that those three elements interact in order to complement each other’s weaknesses and reinforce each other’s strengths. (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
Proceedings of the International Multiconference on Computer Science and Information Technology
editor
Ganzha, M.; Paprzycki, M.; Pełech-Pilichowski, T.; ; and
volume
2
pages
59 - 69
publisher
Polskie Towarzystwo Informatyczne
conference name
International Multiconference on Computer Science and Information Technology
ISSN
1896-7094
language
English
LU publication?
yes
id
c93197f2-1952-43e0-a203-3d2cb542326b (old id 4679146)
date added to LUP
2014-09-25 12:05:03
date last changed
2016-04-16 03:00:01
@inproceedings{c93197f2-1952-43e0-a203-3d2cb542326b,
  abstract     = {We study agents situated in partially observable environments, who do not have sufficient resources to create conformant (complete) plans. Instead, they create plans which are conditional and partial, execute or simulate them, and learn from experience to evaluate their quality. Our agents employ 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 so that the agents can choose the best plan for execution.<br/><br>
We describe an architecture which allows ideas and solutions from several subfields of Artificial Intelligence to be joined together in a controlled and manageable way. In our opinion, no situated agent can achieve true rationality without using at least logical reasoning and learning. In practice, it is clear that pure logic is not able to cope with all the requirements put on reasoning, thus more domain- specific solutions, like planners, are also necessary. Finally, any realistic agent needs a reactive module to meet demands of dynamic environments.<br/><br>
Our architecture is designed in such a way that those three elements interact in order to complement each other’s weaknesses and reinforce each other’s strengths.},
  author       = {Nowaczyk, Sławomir and Malec, Jacek},
  booktitle    = {Proceedings of the International Multiconference on Computer Science and Information Technology},
  editor       = {Ganzha, M. and Paprzycki, M. and Pełech-Pilichowski, T.},
  issn         = {1896-7094},
  language     = {eng},
  pages        = {59--69},
  publisher    = {Polskie Towarzystwo Informatyczne},
  title        = {An Architecture for Resource Bounded Agents},
  volume       = {2},
  year         = {2007},
}