| 1. |
- Malec, Jacek, et al.
(author)
-
Knowledge-Based Reconfiguration of Automation Systems
- 2007
-
In: Automation Science and Engineering, 2007. CASE 2007. IEEE International Conference on. - Piscataway : IEEE Press. - 9781424411542 - 1424411548 ; , s. 170-175
-
Conference paper (peer-reviewed)abstract
- This article describes the work in progress on knowledge-based reconfiguration of a class of automation systems. The knowledge about manufacturing is represented in a number of formalisms and gathered around an ontology expressed in OWL, that allows generic reasoning in Description Logic. In the same time multiple representations facilitate efficient processing by a number of special-purpose reasoning modules, specific for the application domain. At the final stage of reconfiguration we exploit ontology-based rewriting, simplifying creation of the final configuration files.
|
|
| 2. |
- Nowaczyk, Sławomir, 1978-, et al.
(author)
-
An Architecture for Resource Bounded Agents
- 2007
-
In: Proceedings of the International Multiconference on Computer Science and Information Technology. - Katowice : PTI Press. - 1896-7094. ; 2, s. 59-69
-
Conference paper (peer-reviewed)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 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.
|
|
| 3. |
- Nowaczyk, Sławomir, 1978-, et al.
(author)
-
Inductive logic programming algorithm for estimating quality of partial plans
- 2007
-
In: MICAI 2007: Advances in Artificial Intelligence. - Berlin : Springer Berlin/Heidelberg. - 0302-9743 .- 1611-3349. - 9783540766315 - 9783540766308 ; , s. 359-369
-
Conference paper (peer-reviewed)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.
|
|
| 4. |
- Nowaczyk, Sławomir, 1978-, et al.
(author)
-
Learning to evaluate conditional partial plans
- 2007
-
In: ICMLA 2007. - Los Alamitos, Calif. : IEEE Computer Society. - 9780769530697 - 0769530699 ; , s. 235-240
-
Conference paper (peer-reviewed)abstract
- We study agents situated in partially observable environments, who do not have sufficient resources to create conformant plans. Instead, they generate plans which are conditional and partial, execute or simulate them, and learn to evaluate their quality from experience. 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, allowing the agent to execute a good plan. We show results of using PROGOL learning algorithm to distinguish "bad" plans early in the reasoning process, before too many resources are wasted on considering them. We show that additional knowledge needs to be provided before learning can be successful, but argue that the benefits achieved make it worthwhile. Finally, we identify several assumptions made by PROGOL, shared by other similarly universal algorithms, which are well justified in general, but fail to exploit the properties of the class of problems faced by rational agents.
|
|
