A Hybrid Deliberative Layer for Robotic Agents [electronic resource] :Fusing DL Reasoning with HTN Planning in Autonomous Robots / by Ronny Hartanto.
by Hartanto, Ronny [author.]; SpringerLink (Online service).
Material type:
BookSeries: Lecture Notes in Computer Science: 6798Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.Description: XXII, 215 p. online resource.ISBN: 9783642225802.Subject(s): Computer science | Computer Communication Networks | Software engineering | Artificial intelligence | Computer simulation | Computer Science | Artificial Intelligence (incl. Robotics) | Simulation and Modeling | User Interfaces and Human Computer Interaction | Computation by Abstract Devices | Computer Communication Networks | Software EngineeringDDC classification: 006.3 Online resources: Click here to access online
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Springer eBooksSummary: 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.
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| TJ210.2-211.495 (Browse shelf) | Available | ||||
| Long Loan | MAIN LIBRARY | Q334-342 (Browse shelf) | Available |
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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.
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