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A Hybrid Deliberative Layer for Robotic Agents Fusing DL Reasoning with HTN Planning in Autonomous Robots /
The Hybrid Deliberative Layer (HDL) solves the problem that an intelligent agent faces in dealing with a large amount of information which may or may not be useful in generating a plan to achieve a goal. The information, that an agent may need, is acquired and stored in the DL model. Thus, the HDL i...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2011.
|
| Edición: | 1st ed. 2011. |
| Colección: | Lecture Notes in Artificial Intelligence,
6798 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-642-22580-2 | ||
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| 005 | 20221012204647.0 | ||
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| 024 | 7 | |a 10.1007/978-3-642-22580-2 |2 doi | |
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| 100 | 1 | |a Hartanto, Ronny. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 2 | |a A Hybrid Deliberative Layer for Robotic Agents |h [electronic resource] : |b Fusing DL Reasoning with HTN Planning in Autonomous Robots / |c by Ronny Hartanto. |
| 250 | |a 1st ed. 2011. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2011. | |
| 300 | |a XXII, 215 p. |b online resource. | ||
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| 490 | 1 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 6798 | |
| 520 | |a The Hybrid Deliberative Layer (HDL) solves the problem that an intelligent agent faces in dealing with a large amount of information which may or may not be useful in generating a plan to achieve a goal. The information, that an agent may need, is acquired and stored in the DL model. Thus, the HDL is used as the main knowledge base system for the agent. In this work, a novel approach which amalgamates Description Logic (DL) reasoning with Hierarchical Task Network (HTN) planning is introduced. An analysis of the performance of the approach has been conducted and the results show that this approach yields significantly smaller planning problem descriptions than those generated by current representations in HTN planning. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer simulation. | |
| 650 | 0 | |a User interfaces (Computer systems). | |
| 650 | 0 | |a Human-computer interaction. | |
| 650 | 0 | |a Computer science. | |
| 650 | 0 | |a Computer networks . | |
| 650 | 0 | |a Software engineering. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Computer Modelling. |
| 650 | 2 | 4 | |a User Interfaces and Human Computer Interaction. |
| 650 | 2 | 4 | |a Theory of Computation. |
| 650 | 2 | 4 | |a Computer Communication Networks. |
| 650 | 2 | 4 | |a Software Engineering. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642225796 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642225819 |
| 830 | 0 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 6798 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-22580-2 |z Texto Completo |
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