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Revision 5 as of 2011-01-18 15:28:54
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Editor: GabiRoeger
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Revision 6 as of 2011-04-08 10:09:34
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alt(open_list1, open_list2, ..., boost=0) alt(openlists, boost=0)
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 * `open_list1, open_list2, ...` (comma-separated list of [[OpenList]]s): open lists  * `openlists` (list of [[OpenList]]s): open lists
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pareto(evaluator1, evaluator2, ..., pref_only=false, pareto(evals, pref_only=false,
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 * `evaluator1, evaluator2, ...` (comma-separated list of [[ScalarEvaluator]]s): scalar evaluators  * `evals` (list of [[ScalarEvaluator]]s): scalar evaluators
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tiebreaking(evaluator1, evaluator2, ..., pref_only=false, tiebreaking(evals, pref_only=false,
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 * `evaluator1, evaluator2, ...` (comma-separated list of [[ScalarEvaluator]]s): scalar evaluators  * `evals` (list of [[ScalarEvaluator]]s): scalar evaluators

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Open lists

1. Alternation open list

alt(openlists, boost=0)

Alternates between several open lists.

  • openlists (list of OpenLists): open lists

  • boost (int): boost value for sub-open-lists that are restricted to preferred operator nodes

Preferred operators: Preferred operators are only taken from sub-open-lists that do not consider the evaluated state a dead end.

Dead ends: A state is considered a dead end if either all alternated open lists agree that it is a dead end or at least one reliable open list considers is a dead end. A state is never inserted into a sub-open-list that considers it a dead end.

Note: The treatment of dead ends is different from the one described in the technical report "The More, the Merrier: Combining Heuristic Estimators for Satisficing Planning (Extended Version)" (Department of Computer Science at Freiburg University, No. 258, 2010)

2. Pareto open list

pareto(evals, pref_only=false, 
       state_uniform_selection=false)

Selects one of the Pareto-optimal (regarding the sub-evaluators) entry for removal.

  • evals (list of ScalarEvaluators): scalar evaluators

  • pref_only (bool): insert only nodes generated by preferred operators

  • state_uniform_selection (bool): When removing an entry, we select a non-dominated bucket and return its oldest entry. If this option is false, we select uniformly from the non-dominated buckets; if the option is true, we weight the buckets with the number of entries.

3. Standard open list

single(evaluator, pref_only=false)

Standard open list that uses a single evaluator.

  • evaluator (ScalarEvaluator): scalar evaluator

  • pref_only (bool): insert only nodes generated by preferred operators

4. Bucket-based open list

single_buckets(evaluator, pref_only=false)

Bucket-based open list implementation that uses a single evaluator.

  • evaluator (ScalarEvaluator): scalar evaluator

  • pref_only (bool): insert only nodes generated by preferred operators

5. Tie-breaking open list

tiebreaking(evals, pref_only=false, 
            unsafe_pruning=true)
  • evals (list of ScalarEvaluators): scalar evaluators

  • pref_only (bool): insert only nodes generated by preferred operators

  • unsafe_pruning (bool): allow unsafe pruning when the main evaluator regards a state a dead end

FastDownward: OpenList (last edited 2011-04-08 10:09:34 by MoritzGronbach)