Domain dependent knowledge in answer set
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Domain dependent knowledge in answer set
arXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set RANArizona State UniversityandSHEILA MCILRAITHUniversity of Torontohl thus paper we consider three different kinds of domain-dependent control knowledge (temporal, procedural and HTN-based) that are useful in planning. Our approach is declarative anil relics on the language of logic programming with Domain dependent knowledge in answer set answer set semantics (AnsProlog*). AnsProlog* is designed to plan without control knowledge. We show how temporal, procedural and HTN-based control kDomain dependent knowledge in answer set
nowledge can lie incorporated into AnsProlog* !>>• the modular addition of a small number of domain-dependent rules, without the need to modify the plarXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set xperimentation that demonstrates the potential reduction in planning time that can be achieved when procedural domain knowledge is used to -solve planning problems with large plan length.Categories Hid Subject Descriptors: 1.2.8 [Artificial Intelligence]: Problem Solving. Control Methods. and Search Domain dependent knowledge in answer set I'liiu acnetatiofK I 2.3 [Artificial Intelligence]: Deduction aid Tltw-rern Proving- /XỊ/ÍC piụ/nrmvun#; 1.2-1 [Artificial Intelligence] Knowledge PcDomain dependent knowledge in answer set
prtw-ntatioii Formalisms and Methods ■ llepỉVM-ntation lanợuaỵriGeneral Terms: Planning, Control Knowledge, Answer Set PlanningAdditional Key Worth anarXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set anning entails finding a sequence of actions that takes a world from a completely known initial state to a state that satisfies certain goal conditions. The inputs to a corresponding planner are the descriptions (in a compact description language such as STRIPSAuthor’s address: T. c. Son. Computer S Domain dependent knowledge in answer set cience Department, PO Box 30001, MSC cs. New Mexico State University. I-as Cruets, NM 88003, USA.c. Baral and N. Tran, Computer Science anil EngineeriDomain dependent knowledge in answer set
ng. Arizona State University. Tempe, AZ 85287. USA.s. Mcllraith. Department of Comparer Science, University of Toronto. Toronto, Canada M5S 5113. PermarXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set istributed for profit or commercial advantage, the ACM copyright/scrvcr notice, the title of the publication, and its date appear, and notice is given that copying is by permission of the ACM. Inc. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior specif Domain dependent knowledge in answer set ic permission and/or a fee.© 2014 ACM 1529-3785/2014/070041001 $5.00ACM lYanMCtton* 0« c<*np«tntk.iinl Ugfc. Vol. V. No. N. January 2014. Pntes I Api>Domain dependent knowledge in answer set
-a®.2• Tran Cao Son. Chitta Baral, Nam Tran, and Sheila Mcllraith[Fikes and Nilson 1971]) of the effects of actions on t he world, t he description ofarXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set xity of classical planning is known to be undecidable in the general case [Chapman 1987; Erol et al. 1995]. It reduces to PSPACE-complete for finite and deterministic domains [Bylander 1991’. By making certain assumptions such as fixing the length of plans, and requiring actions to be deterministic Domain dependent knowledge in answer set the complexity reduces to NP-complete.The ability to plan is widely recognized to be an important characteristic of an intelligent entity. Thus, whenDomain dependent knowledge in answer set
developing intelligent systems, we often need to incorporate planning capabilities, despite their inherent complexity. Since t he complexity is due toarXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set crux of three common and successful approaches to planning: (i) using heuristics lionet and Geffner 2001; Hoffmann and Nebel 2001; Blum and Furst 1997) that are derived from the problem description, (ii) translating the planning problem into a HUM lei finding problem in a suitable logic and using mo Domain dependent knowledge in answer set del finding techniques for that logic [Kautz and Selman 1998a], and (iii) using domain-dependent control knowledge1 [Bacchus and Kabanza 2000; DohertyDomain dependent knowledge in answer set
ami Kvarnstom 1999; Nau et al.1999]. The use of domain-dependent control knowledge has led to several successful planners, including TLPlan [Bacchus arXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set peaking, planners that iise control knowledge are no longer considered to be classical planners since they require the addition of domain-dependent control knowledge to the problem specification. Nevertheless, such planners are predicted to be t he most scalable types of planning systems in the long Domain dependent knowledge in answer set term [Wilkins and desJardines 2901], In this paper we integrate the second and the third approaches identified above by translating a planning probleDomain dependent knowledge in answer set
m with domain-depeudent control knowledge into a problem of model finding in logic programming.We integrate domain-dependent control knowledge into ouarXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set with which a plan is generated or may result in the generation of plans with particular desirable characteristics. In this respect" our approach is similar in spirit to the planning systems TLPlan • Bacchus and Kabanza 2000], ami TALplan [Doherty and Kvarnstom 1999], but differs from typical Hierarc Domain dependent knowledge in answer set hical Task Network (HTN) planners (e.g., SHOP [Nau et al. 1999]) because HTN planners require integration of domain-dependent control'This is alternatDomain dependent knowledge in answer set
ively referred to in the literature as 'domain^icpciuient knowledge*. 'control knowledge'. 'domain knowledge*. and 'domain constraints*. We also sometarXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set tire domain knowledge is user! by the search program thus controlling the search, our domain knowledge is encoded as a logic program which is directly iuldexl to the logic program encoding planning. In such an approach there is no guarantee that the added ruler will reduce tire search during answer Domain dependent knowledge in answer set set computation: although our experimentation shows that it docs for Large plan lengths. The paper (Huang Ct al. 1999] also comments on this aspect.ACDomain dependent knowledge in answer set
M lYansactions on Computational bogie, Vol. V, No. N. January 2014.Domain Dependent Knowledge in Answer Set Planning ■3knowledge into the specificatioarXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set xplore three kinds of domain control knowledge: temporal knowl edge, procedural knowledge. and HTN based knowledge. Our treatment, of tempo ral knowledge is similar to that used in both the Tl.PIan and TAĨ.plan systems. Our formulation of procedural knowledge is inspired by GOLOG, referred to altern Domain dependent knowledge in answer set atively us a logic programming language, or an action execution language [Levesque et al. I997|. Although our procedural knowledge is similar to the sDomain dependent knowledge in answer set
yntax of a GOT.OG program, how this knowledge is used in planning is quite different. Simi larly. our formulation of 11 TN-bascd knowledge is inspiredarXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set HTN planners. The main difference is that both GOĨ.OG programming and HTN planning rely on the existence of domain-dependent control knowledge within the problem specification and cannot perform classical planning in the absence of this knowledge. In contrast, our approach, which is similar to the Domain dependent knowledge in answer set approach in [Bacchus and Kabanza 2000]. separates the planner module from the domain knowledge (encoding temporal, procedural, or HTN-based knowledge)Domain dependent knowledge in answer set
, and can plan independent of the domain knowledge.To achieve our goal of planning using domain-dependent control knowledge, an important first step iarXiv:cs/0207023v2 [cs.AI] 29 Aug 2005Domain-Dependent Knowledge ill Answer Set PlanningTRAN CAO SONNew Mexico State UniversityCHITTA BARAL and NAM TR Domain dependent knowledge in answer set s to the question of choosing an appropriate language for both reasoning and representation tasks, l-br this we choose the action language Ổ from [Gelfond and Lifschitz 1998] and the language of logic programming with answer set semantics (AnsProlog*) ịlỉaral 2003], also referred to as A-Prolog [Gel Domain dependent knowledge in answer set fond and Leone 2002 . We discuss our choice on p in Section 2. We selected AnsProlog* over other action languages for a number of important reasons, mDomain dependent knowledge in answer set
any of which are listed below. These points are elaborated upon in [Baral 2003|.AnsProlog* is a non monotonic language that is suitable for knowledgeGọi ngay
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