Generative Temporal Planning with Complex Processes
Autonomous vehicles are increasingly being used in mission-critical applications, and robust methods are needed for controlling these inherently unreliable and complex systems. This thesis advocates the use of model-based programming, which allows mission designers to program autonomous missions at...
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| Language: | en_US |
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2004
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| Online Access: | http://hdl.handle.net/1721.1/7113 |
| _version_ | 1826189773727858688 |
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| author | Kennell, Jonathan |
| author_facet | Kennell, Jonathan |
| author_sort | Kennell, Jonathan |
| collection | MIT |
| description | Autonomous vehicles are increasingly being used in mission-critical applications, and robust methods are needed for controlling these inherently unreliable and complex systems. This thesis advocates the use of model-based programming, which allows mission designers to program autonomous missions at the level of a coach or wing commander. To support such a system, this thesis presents the Spock generative planner. To generate plans, Spock must be able to piece together vehicle commands and team tactics that have a complex behavior represented by concurrent processes. This is in contrast to traditional planners, whose operators represent simple atomic or durative actions. Spock represents operators using the RMPL language, which describes behaviors using parallel and sequential compositions of state and activity episodes. RMPL is useful for controlling mobile autonomous missions because it allows mission designers to quickly encode expressive activity models using object-oriented design methods and an intuitive set of activity combinators. Spock also is significant in that it uniformly represents operators and plan-space processes in terms of Temporal Plan Networks, which support temporal flexibility for robust plan execution. Finally, Spock is implemented as a forward progression optimal planner that walks monotonically forward through plan processes, closing any open conditions and resolving any conflicts. This thesis describes the Spock algorithm in detail, along with example problems and test results. |
| first_indexed | 2024-09-23T08:21:20Z |
| id | mit-1721.1/7113 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:21:20Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/71132019-04-09T18:04:40Z Generative Temporal Planning with Complex Processes Kennell, Jonathan AI planning "temporal planning" Autonomous vehicles are increasingly being used in mission-critical applications, and robust methods are needed for controlling these inherently unreliable and complex systems. This thesis advocates the use of model-based programming, which allows mission designers to program autonomous missions at the level of a coach or wing commander. To support such a system, this thesis presents the Spock generative planner. To generate plans, Spock must be able to piece together vehicle commands and team tactics that have a complex behavior represented by concurrent processes. This is in contrast to traditional planners, whose operators represent simple atomic or durative actions. Spock represents operators using the RMPL language, which describes behaviors using parallel and sequential compositions of state and activity episodes. RMPL is useful for controlling mobile autonomous missions because it allows mission designers to quickly encode expressive activity models using object-oriented design methods and an intuitive set of activity combinators. Spock also is significant in that it uniformly represents operators and plan-space processes in terms of Temporal Plan Networks, which support temporal flexibility for robust plan execution. Finally, Spock is implemented as a forward progression optimal planner that walks monotonically forward through plan processes, closing any open conditions and resolving any conflicts. This thesis describes the Spock algorithm in detail, along with example problems and test results. 2004-10-20T20:32:23Z 2004-10-20T20:32:23Z 2004-05-18 AITR-2004-002 http://hdl.handle.net/1721.1/7113 en_US AITR-2004-002 90 p. 15726143 bytes 1269432 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI planning "temporal planning" Kennell, Jonathan Generative Temporal Planning with Complex Processes |
| title | Generative Temporal Planning with Complex Processes |
| title_full | Generative Temporal Planning with Complex Processes |
| title_fullStr | Generative Temporal Planning with Complex Processes |
| title_full_unstemmed | Generative Temporal Planning with Complex Processes |
| title_short | Generative Temporal Planning with Complex Processes |
| title_sort | generative temporal planning with complex processes |
| topic | AI planning "temporal planning" |
| url | http://hdl.handle.net/1721.1/7113 |
| work_keys_str_mv | AT kennelljonathan generativetemporalplanningwithcomplexprocesses |