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Experiments show that the advantage from heuristics and control knowledge changes over the course of the search, and also varies with the specific search algorithm used, demonstrated with branch-and-bound and iterative deepening approaches implemented in the Picat planning module.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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\nCharles University in Prague,Faculty of Mathematics and Physics,Malostranské nám.25,Prague,Czech Republic  \nKeywords:  Search,Branch and Bound,Iterative Deepening,Planning,Control Knowledge,Heuristics,Planning Domain.  \nAbstract:The overall performance of classical planner depends heavily on the domain model which can be enhancedby adding control knowledge and heuristics.Both of them are known techniques which can boost the searchprocess in exchange for some computational overhead needed for their repeated evaluation.Our experimentsshow that the gain from usage of heuristics and control knowledge is evolving throughout the search processand also depends on the type of search algorithm.We demonstrate the idea using the branch-and-bound anditerative deepening search techniques,both implemented in the Picat planning module  \n## 1 INTRODUCTION\n\nAn action is applicable in a given state if all the pre-conditions holds(e.g.the truck t is at the locationl1 and the locations l1 and 12 are connected).A newstate can be obtained from the original state by addingall the positive effects and removing all the negativeeffects of applicable action.  \nAutomated planning is a model-based approach toproblem solving with decades of research history.Themodel is usually used to describe some dynamic sys-tem called a domain.For example we can model asimple world with trucks that transport packages be-tween locations.The description lists possible objecttypes(e.g.trucks,packages,locations),their relationsand properties by means of predicate logic.For in-stance we can define predicate connected(Loc,Loc)which describes the fact that two locations are con-nected by road and predicate in(Pkg,Truck)to de-scribe that a package is loaded in a truck.The state ofthe world in a particular situation is then described asa set of grounded predicates.  \nThe predicates and the operators together consti-tutes a planning domain.In order to specify a plan-ning problem instance we need to define some initialstate and a goal condition.The planning domain withan instance of the planning problem define a searchspace.The solution for the planning problem instanceis a sequence of actions that leads from the initial stateto some state where the goal condition is satisfied.The approach just described is called classical plan-ning(Ghallab et al.,2004).  \nThe model used in the classical planning providesground for this paper.Since the International Plan-ning Competitions(IPC's)the Planning Domain Def-inition Language(PDDL)(McDermott et al.,1998)is the most commonly used language for describingplanning domains and problems.The domain modelsdeveloped in PDDL for IPC use the “physics only”modeling principle where the model describes onlyhow actions change the state of the world but theydo not indicate how the problem should be solved.The automated planners working with PDDL are thencalled domain-independent planners as they do notexpect any domain specific information in the model.  \nIn order to model dynamics of the system we needto describe possible state transitions.We can do thisby describing so called operators.An operator can bebest understood as a template that can describe manysimilar state transitions that differs only in their pa-rameters.In planning a state transition is called anaction and it consists of four parts.Here is an exam-ple action drive(t,11,12)describing the move ofthe truck t from location l1 to location l2:  \n● the ordered list of parameters:[t,11,12]  \n● the list of preconditions:[at(t,11),connected(11,12)]  \nEven for relatively small problem instances thesearch space is usually vast due to combinatorial com-plexity.There are both domain independent and do-  \n● the list of positive effects:[at(t,12)]  \n● the list of negative effects:[at(t,11)]  \n552  \nmain specific techniques designed to prune the searchspace and to direct the search algorithm ","cbCainSu7Aqf3Cmk","https://ap.wps.com/l/cbCainSu7Aqf3Cmk","pdf",333186,"English","# 1 INTRODUCTION\n## Automated planning fundamentals\n## Planning domains, states, and operators\n## Heuristic search and domain control knowledge\n## Focus of this paper","[{\"question\":\"What key factors influence the performance of classical planners?\",\"answer\":\"Classical planner performance depends heavily on the domain model, which can be enhanced by adding control knowledge and heuristics.\"},{\"question\":\"How do heuristics and control knowledge improve search?\",\"answer\":\"They boost the search process by helping guide exploration, at the cost of computational overhead for repeated evaluation.\"},{\"question\":\"How does the benefit of heuristics and control knowledge behave during search?\",\"answer\":\"The gain from using heuristics and control knowledge evolves throughout the search process and depends on the type of search algorithm.\"}]","The Benefit of Control Knowledge and Heuristics During Search in Planning | PDF",1791077997]