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AltAlt: Combining Graphplan and Heuristic State Search

Biplav Srivastava
2001 The AI Magazine  
Conclusion The ALTALT planning system is based on a combination of GRAPHPLAN and heuristic state space search technology.  ...  This planning graph structure is then fed to a heuristic extractor module that is capable of extracting a variety of effective and admissible ALTALT Combining Graphplan and Heuristic State Search tions  ... 
doi:10.1609/aimag.v22i3.1579 dblp:journals/aim/Srivastava01 fatcat:kilgizv7yrahzoeuzzjt4cxk2i

AltAltp: Online Parallelization of Plans with Heuristic State Search

R. Sanchez, S. Kambhampati
2003 The Journal of Artificial Intelligence Research  
We present a variant of our heuristic state search planner AltAlt, called AltAltp which generates parallel plans by using greedy online parallelization of partial plans.  ...  Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning.  ...  Background on AltAlt AltAlt system is based on a combination of Graphplan [1; 5; 12] and heuristic state space search [2; 3; 7] technology.  ... 
doi:10.1613/jair.1168 fatcat:o3cr3xzv6vbppihar6tu22aci4

Parallelizing State Space Plans Online

Romeo Sanchez Nigenda, Subbarao Kambhampati
2003 International Joint Conference on Artificial Intelligence  
on a combination of Graphplan [Blum and Furst, 1997 ] and heuristic state space search [Haslum and Geffner, 2000] technology.  ...  We introduce a variant of our heuristic state search planner AltAlt, which generates parallel plans by using greedy online parallelization of partial plans.  ...  planners that search in the space of states are overwhelmed by this task [Haslum and Geffner, 2000] .  ... 
dblp:conf/ijcai/NigendaK03 fatcat:ooek2yg4qramtaq43mhq2q5i7u

Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search

XuanLong Nguyen, Subbarao Kambhampati, Romeo S. Nigenda
2002 Artificial Intelligence  
Most recent strides in scaling up planning have centered around two competing themesdisjunctive planners, exemplified by Graphplan, and heuristic state search planners, exemplified by UNPOP, HSP and HSP-r  ...  The results place AltAlt in the top tier of the competition planners-outperforming both Graphplan based and heuristic search based planners.  2001 Published by Elsevier Science B.V. ✩ Preliminary versions  ...  Do, Hector Geffner, Jillian Nottingham, Ioannis Refanidis, David Smith, Biplav Srivastava and Terry Zimmerman for helpful discussions and feedback.  ... 
doi:10.1016/s0004-3702(01)00158-8 fatcat:ln35hyz7fncszf53vebxqnfhpi

A Heuristic for Planning Based on Action Evaluation [chapter]

Dimitris Vrakas, Ioannis Vlahavas
2002 Lecture Notes in Computer Science  
The heuristic, which has been further refined by a goal-ordering technique, has been implemented in AcE (Action Evaluation), a state space heuristic planner, and thoroughly tested on a variety of toy problems  ...  This paper proposes a domain independent heuristic for state space planning, which is based on action evaluation.  ...  The distances of the independent facts are then combined to provide the search with estimates for the distances of whole states.  ... 
doi:10.1007/3-540-46148-5_7 fatcat:aif45f35uneidoixzdylhmrfee

Using Available Memory to Transform Graphplan's Search

Terry Zimmerman, Subbarao Kambhampati
2003 International Joint Conference on Artificial Intelligence  
We present a major variant of the Graphplan algorithm that employs available memory to transform the depth-first nature of Graphplan's search into an iterative state space view in which heuristics can  ...  By heuristically pruning this search space PEGG produces plans comparable to Graphplan's in makespan, at speeds approaching state-of-the-art heuristic serial planners.  ...  Motivation and Approach Despite the recent dominance of heuristic state-search planners over Graphplan-style planners, the Graphplan approach [Blum and Furst 1997] is still one of the most effective  ... 
dblp:conf/ijcai/ZimmermanK03 fatcat:gkm6e36xincirj552bcend3eoi

Using Memory to Transform Search on the Planning Graph

T. Zimmerman, S. Kambhampati
2005 The Journal of Artificial Intelligence Research  
We demonstrate that distance-based, state space heuristics can be adapted to informed traversal of the search trace used by the second class of planners and develop an augmentation targeted specifically  ...  By adopting beam search on the search trace we then show that virtually optimal parallel plans can be generated at speeds quite competitive with a modern heuristic state space planner.  ...  space planner using the 'adjusted-sum heuristic PEGG: bounded PE search, best 50 search segments visited in each search episode, as ordered by adjsum-flux state space heuristic AltAlt: Lisp version  ... 
doi:10.1613/jair.1477 fatcat:sme632ndrnf6xjvpwe6dsfxar4

Heuristic Search Planner 2.0

Blai Bonet, Hector Geffner
2001 The AI Magazine  
), the search algorithm used (variants of best-first search), and the heuristic function extracted from the problem representation.  ...  HSP2.0 is a domain-independent planning algorithm that implements the family of heuristic search planners that are characterized by the state space that is searched (either progression or regression space  ...  ALTALT, however, derives the heuristic from the plan graph constructed by a GRAPHPLAN-type procedure and uses this heuristic to drive a regression search from the goal.  ... 
doi:10.1609/aimag.v22i3.1576 dblp:journals/aim/BonetG01 fatcat:rhve2y2tizcpxiyeakjkvmmoei

Planning the project management way: Efficient planning by effective integration of causal and resource reasoning in RealPlan

Biplav Srivastava, Subbarao Kambhampati, Minh B. Do
2001 Artificial Intelligence  
to direct planner search.  ...  The current work shows that the above strategy severely curtails the scale-up potential of existing state of the art planners which can be overcome by leveraging the loose coupling.  ...  Acknowledgements We wish to thank Prof. van Beek for help with his CSP library and solvers.  ... 
doi:10.1016/s0004-3702(01)00122-9 fatcat:li3frpdzezf5xas4l5jaysgz5a

The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning

I. Refanidis, I. Vlahavas
2001 The Journal of Artificial Intelligence Research  
Then, in the search phase, these estimates are used in order to further estimate the distance between each intermediate state and the goals, guiding so the search process in a forward direction and on  ...  Moreover, it presents several methods of improving the efficiency of the heuristic, by enriching the representation and by reducing the size of the problem.  ...  , Maria Fox, Romeo Sanchez Nigenda, XuanLong Nguyen and Subbarao Kambhampati.  ... 
doi:10.1613/jair.893 fatcat:dm47hxonqjeprbaxs363xcvnge

Learning from planner performance

Mark Roberts, Adele Howe
2009 Artificial Intelligence  
The studies described in the paper have multiple goals related to analyzing and extending the state of the art.  ...  In a third analysis, we apply the data to an existing explanatory model linking the relationship between the search space and planner performance.  ...  We also thank the ICAPS community -notably Maria Fox, Jeremy Frank, Jörg Hoffmann, and David Smith -for conversations and suggestions that helped guide and shape this work.  ... 
doi:10.1016/j.artint.2008.11.009 fatcat:ucmpsbkuh5axfpyowbobf2cfo4


Dimitris Vrakas, Ioannis Vlahavas
2005 Computational intelligence  
Moreover, the system is equipped with a fact-ordering technique and two methods for problem simplification that limit the search space and guide the algorithm to the most promising states.  ...  This paper presents Hybrid-AcE, a domain-independent planning system that combines search in both directions utilizing a complex criterion that monitors the progress of the search, to switch between them  ...  Motivated by the conclusions stated above we developed Hybrid AcE, a heuristic statespace planner, which combines search in both directions.  ... 
doi:10.1111/j.1467-8640.2005.00275.x fatcat:qgzggj6ibzh7fcrkikay4c2ot4

Approximation Algorithms and Heuristics for Classical Planning [chapter]

Jeremy Frank, Minh Do, J. Benton
2018 Handbook of Approximation Algorithms and Metaheuristics, Second Edition  
In the Graphplan planner [BF95] , a simple plan-space regression search is used, with no particular emphasis on action selection heuristics.  ...  In state space search, heuristics are evaluated on states or sets of propositions, and estimate the minimal distance to the search objective.  ... 
doi:10.1201/9781351236423-39 fatcat:qqcfrb7gkbfohdgu2sg23sqbmi


2005 International journal on artificial intelligence tools  
just being a user friendly environment for executing the underlying planner, the tool serves as a unified planning environment for encoding a new problem problem, solving it, visualizing the solution and  ...  The tool consists of various sub-systems, each one accompanied by a graphical interface, that collaborate with each other and assist the user, whether he is a knowledge engineer, a domain expert, an academic  ...  search the states of the search for violations of orderings between the facts of either the initial state or the goals, depending on the direction of the search.  ... 
doi:10.1142/s0218213005002491 fatcat:vgg4o54adnbebabxkj2qwjwkqa

Planning Graph Heuristics for Belief Space Search

D. Bryce, S. Kambhampati, D. E. Smith
2006 The Journal of Artificial Intelligence Research  
To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states.  ...  We give several techniques to aggregate state distances and their associated properties.  ...  Do, Romeo Sanchez, Terry Zimmermam, Satish Kumar Thittamaranahalli, and Will Cushing for helpful discussions and feedback, Jussi Rintanen for help with the YKA planner, and Piergiorgio Bertoli for help  ... 
doi:10.1613/jair.1869 fatcat:a467wulbyncglf4rnvhcljkcaq
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