Recovering from plan failure using a layered architecture:
Abstract: "Planners which are to operate in uncertain environments must be able to recover from failures when they occur. Little work has been done in the past on incorporating recovery strategies into planning systems and deciding which recovery strategy to use when. This paper describes a num...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Edinburgh
1991
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| Schriftenreihe: | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper
524 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "Planners which are to operate in uncertain environments must be able to recover from failures when they occur. Little work has been done in the past on incorporating recovery strategies into planning systems and deciding which recovery strategy to use when. This paper describes a number of recovery strategies which are useful either generally in planning domains or in multiple agent tasks. It discusses Moore's [3] system, which uses a rigid ordering to choose the next recovery strategy to try, and explains why this technique produces undesirable results for both traditional planning domains and the simulation of human planning Then it discusses a simple extension to Moore's work [1] which corrects the problem by allowing a recovery strategy to be chosen based on an estimate of the effort needed to complete the task using that strategy, but which is difficult to work with because it 'hard-wires' the decisions into the code. A change in approach is required which makes the recovery strategies techniques about which the system makes decisions, rather than outside procedures that the system calls upon as needed. In effect, the recovery strategies, along with normal planning and execution, make up a set of meta-operations which can be planned analogously to domain level plans. The paper describes a system which operates in this way by using a layered planning architecture modelled on Stefik's MOLGEN [5] By continuing the abstraction of layers upwards, further levels of operators meta-plan the choice of a recovery strategy and the choice of the way of choosing a recovery strategy. This system has the advantages that it is easy to both change the way in which recovery strategies are employed and add new recovery strategies, simply by changing the operator definitions at the higher levels. The system is implemented in a task- oriented dialogue domain. |
| Beschreibung: | 10 S. |
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| 490 | 1 | |a University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |v 524 | |
| 520 | 3 | |a Abstract: "Planners which are to operate in uncertain environments must be able to recover from failures when they occur. Little work has been done in the past on incorporating recovery strategies into planning systems and deciding which recovery strategy to use when. This paper describes a number of recovery strategies which are useful either generally in planning domains or in multiple agent tasks. It discusses Moore's [3] system, which uses a rigid ordering to choose the next recovery strategy to try, and explains why this technique produces undesirable results for both traditional planning domains and the simulation of human planning | |
| 520 | 3 | |a Then it discusses a simple extension to Moore's work [1] which corrects the problem by allowing a recovery strategy to be chosen based on an estimate of the effort needed to complete the task using that strategy, but which is difficult to work with because it 'hard-wires' the decisions into the code. A change in approach is required which makes the recovery strategies techniques about which the system makes decisions, rather than outside procedures that the system calls upon as needed. In effect, the recovery strategies, along with normal planning and execution, make up a set of meta-operations which can be planned analogously to domain level plans. The paper describes a system which operates in this way by using a layered planning architecture modelled on Stefik's MOLGEN [5] | |
| 520 | 3 | |a By continuing the abstraction of layers upwards, further levels of operators meta-plan the choice of a recovery strategy and the choice of the way of choosing a recovery strategy. This system has the advantages that it is easy to both change the way in which recovery strategies are employed and add new recovery strategies, simply by changing the operator definitions at the higher levels. The system is implemented in a task- oriented dialogue domain. | |
| 650 | 7 | |a Computer software |2 sigle | |
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| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Planning | |
| 810 | 2 | |a Department of Artificial Intelligence: DAI research paper |t University <Edinburgh> |v 524 |w (DE-604)BV010450646 |9 524 | |
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| building | Verbundindex |
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| format | Book |
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| id | DE-604.BV010453614 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:52:48Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006966354 |
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| owner_facet | DE-91G DE-BY-TUM |
| physical | 10 S. |
| publishDate | 1991 |
| publishDateSearch | 1991 |
| publishDateSort | 1991 |
| record_format | marc |
| series2 | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |
| spelling | Carletta, Jean Verfasser aut Recovering from plan failure using a layered architecture Edinburgh 1991 10 S. txt rdacontent n rdamedia nc rdacarrier University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 524 Abstract: "Planners which are to operate in uncertain environments must be able to recover from failures when they occur. Little work has been done in the past on incorporating recovery strategies into planning systems and deciding which recovery strategy to use when. This paper describes a number of recovery strategies which are useful either generally in planning domains or in multiple agent tasks. It discusses Moore's [3] system, which uses a rigid ordering to choose the next recovery strategy to try, and explains why this technique produces undesirable results for both traditional planning domains and the simulation of human planning Then it discusses a simple extension to Moore's work [1] which corrects the problem by allowing a recovery strategy to be chosen based on an estimate of the effort needed to complete the task using that strategy, but which is difficult to work with because it 'hard-wires' the decisions into the code. A change in approach is required which makes the recovery strategies techniques about which the system makes decisions, rather than outside procedures that the system calls upon as needed. In effect, the recovery strategies, along with normal planning and execution, make up a set of meta-operations which can be planned analogously to domain level plans. The paper describes a system which operates in this way by using a layered planning architecture modelled on Stefik's MOLGEN [5] By continuing the abstraction of layers upwards, further levels of operators meta-plan the choice of a recovery strategy and the choice of the way of choosing a recovery strategy. This system has the advantages that it is easy to both change the way in which recovery strategies are employed and add new recovery strategies, simply by changing the operator definitions at the higher levels. The system is implemented in a task- oriented dialogue domain. Computer software sigle Künstliche Intelligenz Artificial intelligence Planning Department of Artificial Intelligence: DAI research paper University <Edinburgh> 524 (DE-604)BV010450646 524 |
| spellingShingle | Carletta, Jean Recovering from plan failure using a layered architecture Computer software sigle Künstliche Intelligenz Artificial intelligence Planning |
| title | Recovering from plan failure using a layered architecture |
| title_auth | Recovering from plan failure using a layered architecture |
| title_exact_search | Recovering from plan failure using a layered architecture |
| title_full | Recovering from plan failure using a layered architecture |
| title_fullStr | Recovering from plan failure using a layered architecture |
| title_full_unstemmed | Recovering from plan failure using a layered architecture |
| title_short | Recovering from plan failure using a layered architecture |
| title_sort | recovering from plan failure using a layered architecture |
| topic | Computer software sigle Künstliche Intelligenz Artificial intelligence Planning |
| topic_facet | Computer software Künstliche Intelligenz Artificial intelligence Planning |
| volume_link | (DE-604)BV010450646 |
| work_keys_str_mv | AT carlettajean recoveringfromplanfailureusingalayeredarchitecture |