Planning domain definition using GIPO

Simpson, R.M.; Kitchin, Diane E.; McCluskey, T.L.

doi:10.1017/s0269888907001063

Cited by 41 publications

(47 citation statements)

References 14 publications

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“…First, communication between the protocol expert and the knowledge engineer during the first stage is easier if the model relies on a CIG-based language than if it is done with a cumbersome planning domain representation (e.g., in HPDL). Second, we ease the traditional di culty of modeling planning domains directly with traditional planning languages and the knowledge engineering requirements for the application of AI planning techniques [45,46]. This is mainly because CIG-based representations are closer to end users in the healthcare domain, and they can be reviewed more easily, or even formally checked by critiquing [47].…”

Section: Discussionmentioning

confidence: 99%

Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks

González-Ferrer¹,

Teije²,

Fdez-Olivares³

et al. 2013

Artificial Intelligence in Medicine

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Objective: This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To this end, a translation from a time-annotated computerinterpretable guideline (CIG) model of a clinical protocol into a temporal hierarchical task network (HTN) planning domain is presented.Methods and Material: The proposed method uses a knowledge-driven reasoning process to translate knowledge previously described in a CIG into a corresponding HTN Planning and Scheduling domain, taking advantage of HTNs known ability to i) dynamically cope with temporal and resource constraints, and ii) automatically generate customized plans. The proposed method, focusing on the representation of temporal knowledge and based on the identification of workflow and temporal patterns in a CIG, makes it possible to automatically generate time-annotated and resource-based care pathways tailored to the needs of any possible patient profile.Results: The proposed translation is illustrated through a case study based on a 70 pages long clinical protocol to manage Hodgkin's disease, developed by the Spanish Society of Pediatric Oncology. We show that an HTN planning domain can be generated from the corresponding specification of the protocol in the Asbru language, providing a running example of this translation. Furthermore, the correctness of the translation is checked and also the management of ten di↵erent types of temporal patterns represented in the protocol. By interpreting the automatically generated domain with a state-of-art HTN planner, a time-annotated care pathway is automatically obtained, customized for the patient's and institutional needs. The generated care pathway can then be used by clinicians to plan and manage the patients long-term care.Conclusion: The described methodology makes it possible to automatically generate patient-tailored care pathways, leveraging an incremental knowledge-driven engineering process that starts from the expert knowledge of medical professionals. The presented approach makes the most of the strengths inherent in both CIG languages and HTN planning and scheduling techniques: for the former, knowledge acquisition and representation of the original clinical protocol, and for the latter, knowledge reasoning capabilities and an ability to deal with complex temporal and resource constraints. Moreover, the proposed approach provides immediate access to technologies such as business process management (BPM) tools, which are increasingly being used to support healthcare processes.

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Section: Discussionmentioning

confidence: 99%

Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks

González-Ferrer¹,

Teije²,

Fdez-Olivares³

et al. 2013

Artificial Intelligence in Medicine

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“…The Graphical Interface for Planning with Objects (GIPO) (Simpson et al, 2007) is based on objectcentred languages OCL and OCL h . These formal languages exploit the idea that a set of possible states of objects are defined first, before action (operator) definition (McCluskey and Kitchin, 1998).…”

Section: Related Workmentioning

confidence: 99%

KEWI - A Knowledge Engineering Tool for Modelling AI Planning Tasks

Wickler

Chrpa

McCluskey

2014

Proceedings of the International Conference on Knowledge Engineering and Ontology Development

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Abstract:This paper introduces the Knowledge Engineering Web Interface (KEWI) which primarily aims to be used for modelling automated planning tasks in a semi-formal framework. The conceptual model used to represent the declarative and procedural knowledge in KEWI is described formally. The model consists of three layers: a rich ontology, a model of basic actions, and more complex methods. It is this structured conceptual model based on the rich ontology that facilitates knowledge engineering. The focus of this paper is to show how the central knowledge model used in KEWI differs from a model directly encoded in PDDL, the language accepted by most existing planning engines. Specifically, the rich ontology enables a more concise and natural style of representation. For operational use, KEWI automatically generates PDDL. Initial experiments show that the generated PDDL can be processed by a planner without incurring significant drawbacks.

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“…To do this we extend GIPO's Opmaker system (Simpson et al, 2007) so that it can induce representations of actions from training sequences without intermediate state information and without requiring large numbers of examples. This method shows the potential for considerably reducing the burden of knowledge engineering, in that it would be possible to embed the method into an autonomous program (agent) which is required to do planning.…”

Section: Introductionmentioning

confidence: 99%

“…This paper describes Opmaker2, an extension of the earlier Opmaker system (McCluskey et al, 2002), in that the latter is an interactive learning tool, whereas the former can be run in batch mode without the need for user assistance. Opmaker was implemented within the GIPO system (Simpson et al, 2007), an experimental tools environment for use in the acquisition of AI planning knowledge, containing a wide range of engineering and validation tools. GIPO was based on the planning language of OCL (McCluskey and Porteous, 1996).…”

Section: Introductionmentioning

confidence: 99%

“…The problem of formulating domain models containing dynamic knowledge regarding actions is a barrier to the widespread uptake of AI planning, because of the difficulty in acquiring and maintaining them. Here we postulate a method which inputs a partial domain model (one without knowledge of domain actions) and training solution sequences to planning tasks, and outputs the full domain model, including heuristics that can be used to make plan generation more efficient.To do this we extend GIPO's Opmaker system (Simpson et al, 2007) so that it can induce representations of actions from training sequences without intermediate state information and without requiring large numbers of examples. This method shows the potential for considerably reducing the burden of knowledge engineering, in that it would be possible to embed the method into an autonomous program (agent) which is required to do planning.…”

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confidence: 99%

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Automated Acquisition of Action Knowledge

McCluskey¹,

Cresswell

Richardson

et al. 2009

Proceedings of the International Conference on Agents and Artificial Intelligence

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Abstract:AI planning engines require detailed specifications of dynamic knowledge of the domain in which they are to operate, before they can function. Further, they require domain-specific heuristics before they can function efficiently. The problem of formulating domain models containing dynamic knowledge regarding actions is a barrier to the widespread uptake of AI planning, because of the difficulty in acquiring and maintaining them. Here we postulate a method which inputs a partial domain model (one without knowledge of domain actions) and training solution sequences to planning tasks, and outputs the full domain model, including heuristics that can be used to make plan generation more efficient.To do this we extend GIPO's Opmaker system (Simpson et al., 2007) so that it can induce representations of actions from training sequences without intermediate state information and without requiring large numbers of examples. This method shows the potential for considerably reducing the burden of knowledge engineering, in that it would be possible to embed the method into an autonomous program (agent) which is required to do planning. We illustrate the algorithm as part of an overall method to acquire a planning domain model, and detail results that show the efficacy of the induced model.

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Planning domain definition using GIPO

Cited by 41 publications

References 14 publications

Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks

Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks

KEWI - A Knowledge Engineering Tool for Modelling AI Planning Tasks

Automated Acquisition of Action Knowledge

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