Enhancing the Context-Enhanced Additive Heuristic with Precedence Constraints

Cai, Dunbo; Hoffmann, Jörg; Helmert, Malte

doi:10.1609/icaps.v19i1.13342

Cited by 14 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another technique that offers a great potential is the combination of this work with that of landmarks. Or, similarly, the work on reasonable orders, such as the one that creates a goal agenda (Hoffmann 2001) or to add precedence constraints in heuristic computations (Cai, Hoffmann, and Helmert 2009). In this case, they can be used to impose additional constraints in the backwards goal generation, taking advantage of the strong order interactions among goals.…”

Section: Discussionmentioning

confidence: 99%

Using Backwards Generated Goals for Heuristic Planning

Alcázar

Borrajo

López

2010

ICAPS

View full text Add to dashboard Cite

Forward State Planning with Reachability Heuristics is arguably the most successful approach to Automated Planning up to date. In addition to an estimation of the distance to the goal, relaxed plans obtained with such heuristics provide the search with useful information such as helpful actions and look-ahead states. However, this information is extracted only from the beginning of the relaxed plan. In this paper, we propose using information extracted from the last actions in the relaxed plan to generate intermediate goals backwards. This allows us to use information from previous computations of the heuristic and reduce the depth of the search tree.

show abstract

Section: Discussionmentioning

confidence: 99%

Using Backwards Generated Goals for Heuristic Planning

Alcázar

Borrajo

López

2010

ICAPS

View full text Add to dashboard Cite

show abstract

“…Can we design heuristics that are better at dealing with the life-cycle of substitution nodes and distractors? It is easy to see that the problem of not noticing them in the initial state is present for most known heuristics (Helmert 2004;Richter, Helmert, and Westphal 2008;Karpas and Domshlak 2009;Helmert and Domshlak 2009;Cai, Hoffman, and Helmert 2009). The two notable exceptions are the h m family, and abstraction heuristics.…”

Section: Search Techniquesmentioning

confidence: 99%

Waking Up a Sleeping Rabbit: On Natural-Language Sentence Generation with FF

Koller

Hoffmann

2010

ICAPS

Self Cite

View full text Add to dashboard Cite

We present a planning domain that encodes the problem of generating natural language sentences. This domain has a number of features that provoke fairly unusual behavior in planners. In particular, hitherto no existing automated planner was sufficiently effective to be of practical value in this application. We analyze in detail the reasons for ineffectiveness in FF, resulting in a few minor implementation fixes in FF's preprocessor, and in a basic reconfiguration of its search options. The performance of the modified FF is up to several orders of magnitude better than that of the original FF, and for the first time makes automated planners a practical possibility for this application. Beside thus highlighting the importance of preprocessing and automated configuration techniques, we show that the domain still poses several interesting challenges to the development of search heuristics.

show abstract

“…This success notwithstanding, the delete relaxation has significant pitfalls, e. g., in planning with non-replenishable resources, whose consumption is completely ignored within the relaxation. Recent years thus have seen active research aiming at taking some deletes into account, e. g. (Fox and Long 2001;Gerevini, Saetti, and Serina 2003;Helmert 2004;Helmert and Geffner 2008;Keyder and Geffner 2008;Baier and Botea 2009;Cai, Hoffmann, and Helmert 2009;Keyder, Hoffmann, and Haslum 2012; Copyright c 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.…”

Section: Introductionmentioning

confidence: 99%

Red-Black Relaxed Plan Heuristics Reloaded

Katz

Hoffmann

2021

SOCS

View full text Add to dashboard Cite

Despite its success, the delete relaxation has significant pitfalls. In an attempt to overcome these pitfalls, recent work has devised so-called red-black relaxed plan heuristics, where red variables take the relaxed semantics (accumulating their values), while black variables take the regular semantics. These heuristics were shown to significantly improve over standard delete relaxation heuristics. However, the experiments also brought to light a major weakness: Being based on repairing fully delete-relaxed plans, the returned estimates depend on arbitrary choices made in such plans. This can lead to huge over-estimation in arbitrary subsets of states. Here we devise a new red-black planning method not based on repairing relaxed plans, getting rid of much of this variance. Our experiments show a significant improvement over previous red-black relaxed plan heuristics, and other related methods.

show abstract

Enhancing the Context-Enhanced Additive Heuristic with Precedence Constraints

Cited by 14 publications

References 6 publications

Using Backwards Generated Goals for Heuristic Planning

Using Backwards Generated Goals for Heuristic Planning

Waking Up a Sleeping Rabbit: On Natural-Language Sentence Generation with FF

Red-Black Relaxed Plan Heuristics Reloaded

Contact Info

Product

Resources

About