Engineering and compiling planning domain models to promote validity and efficiency

McCluskey, T.L.; Porteous, Julie

doi:10.1016/s0004-3702(97)00034-9

Cited by 38 publications

(41 citation statements)

References 10 publications

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“…The importance of V&V in domain models for planning has long been recognised in both domain independent work [24] and more specifically in space applications [28]. Ways to assess the quality of a domain model can be classed into two typesdynamic and static-in a similar way to investigating program code quality.…”

Section: Model Qualitymentioning

confidence: 99%

Engineering Knowledge for Automated Planning

McCluskey

Vaquero

Vallati

2017

Proceedings of the Knowledge Capture Conference

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Automated planning is a prominent Artificial Intelligence challenge, as well as being a common capability requirement for intelligent autonomous agents. A critical aspect of what is called domainindependent planning, is the application knowledge that must be added to the planner to create a complete planning application. This is made explicit in (i) a domain model, which is a formal representation of the persistent domain knowledge, and (ii) an associated problem instance, containing the details of the particular problem to be solved. Both these components are used by automated planning engines for reasoning, in order to synthesize a solution plan. Formulating knowledge for use in planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers significantly influence the quality of the resulting planning application. On top of that, a notion of quality of the knowledge captured within a domain model is missing; it is therefore hard to provide useful guidelines to knowledge engineers. This paper raises some issues relating to the engineering of application knowledge for automated planning, focussing on the domain model. It uses the idea of a domain model as a formal specification of a domain, and considers what it means to measure the quality of such a specification. To do this it proposes definitions of the attributes of a domain model and its encoding language, which are needed by the automated planning community in order to improve tools for supporting the engineering of planning knowledge, and to advance toward a shared and inclusive definition of quality of domain models.

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Section: Model Qualitymentioning

confidence: 99%

Engineering Knowledge for Automated Planning

McCluskey

Vaquero

Vallati

2017

Proceedings of the Knowledge Capture Conference

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“…Using macro-actions (or macro-operators) in planning is quite popular [30,5,7,28]. Macro-actions represent sequences of actions but they are encoded like 'normal' actions.…”

Section: Macro-action Schemementioning

confidence: 99%

On the completeness of replacing primitive actions with macro-actions and its generalization to planning operators and macro-operators

Chrpa

McCluskey

Osborne

2015

AIC

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Automated planning, which deals with the problem of generating sequences of actions, is an emerging research topic due to its potentially wide range of realworld application domains. As well as developing and improving planning engines, the acquisition of domainspecific knowledge is a promising way to improve the planning process. Domain-specific knowledge can be encoded into the modelling language that a range of planning engines can accept. This makes encoding domain-specific knowledge planner-independent, and entails reformulating the domain models and/or problem specifications. While many encouraging practical results have been derived from such reformulation methods (e.g learning macro-actions), little attention has been paid to the theoretical properties such as completeness (keeping solvability of reformulated problems). In this paper, we focus on a special caseremoving primitive actions replaced by macro-actions. We provide a theoretical study and come up with conditions under which it is safe to remove primitive actions, so completeness of reformulation is preserved. We extend this study also for planning operators (actions are instances of operators).

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“…To this end, the Core Plan Representation (CPR) [Pease and Carrico, 1996] was developed. The CPR establishes a conceptual framework for the rich representation of plans that are to be communicated between agents.…”

Section: The Representationmentioning

confidence: 99%

“…A rigorous method for problem formulation in the case of planning domains was presented in [McCluskey and Porteous, 1997]. In the second step of their methodology types are extracted from an informal description of a planning domain.…”

Section: Related Workmentioning

confidence: 99%

Plan Representations for Distributed Planning and Execution

Wickler¹

2011

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information , 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. (From -To) REPORT DATE (DD-MM-YYYY) 15-08-2011 REPORT TYPE Final Report DATES COVERED SPONSOR/MONITOR'S ACRONYM(S)AFRL/AFOSR/RSW (EOARD) SPONSOR/MONITOR'S REPORT NUMBER(S)AFRL-AFOSR-UK-TR-2011-0047 DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTThis report results from a contract tasking University of Edinburgh as follows: The final project report describes work completed towards a new framework for distributed multi-agent planning and plan execution. The first step focuses on the plan representation that needs to be sufficiently rich to allow the sharing of plans between humans, software systems, and robotic entities. The basis for this representation is the Core Plan Representation developed in the mid 90s. This representation and its roots in Artificial Planning research are examined in section 3. The focus of this representation is the concept of a plan. Plans are executed by agents, and one of the dominant models of agency in agent research describes three mental attitudes of an agent: beliefs, desires and intentions. In section 4 we shall examine these concepts attempt to merge them with the plan representation. The combined ontology will then be further refined in section 5, introducing concepts that will be required for a sharable plan representation intended for distributed multi-agent planning and plan execution. The report then goes on to describe the software support for this representation, which has been developed as an extension to MediaWiki. The next part of this report defines a number of features that can be used to characterize planning domains, namely domain types, relation fluency, inconsistent effects and reversible actions. These features can be used to provide additional information about the operators defined in a strips-like planning domain. Furthermore, the values of these features may be extracted automatically; efficient algorithms for this are described in this report. Alternatively, where these values are specified explicitly by the domain author, the extracted values can be used to validate the consistency of the domain, thus supporting the k...

show abstract

Engineering and compiling planning domain models to promote validity and efficiency

Cited by 38 publications

References 10 publications

Engineering Knowledge for Automated Planning

Engineering Knowledge for Automated Planning

On the completeness of replacing primitive actions with macro-actions and its generalization to planning operators and macro-operators

Plan Representations for Distributed Planning and Execution

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