The concept and implementation of skeletal plans

Friedland, Peter; Iwasaki, Yumi

doi:10.1007/bf00244995

Cited by 131 publications

(55 citation statements)

References 21 publications

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“…Case-based planning differs from other approaches to planning and problem solving (e.g., Fikes & Nilsson, 1971;Friedland & Iwasaki, 1985;Newell & Simon, 1972;Sacerdoti, 1975;Sussman, 1975;Tate, 1977;Wilensky, 1980) in three areas: in initial plan building, in the reaction to plan failures, .and in the vocabulary for desribing and storing plans. Although there is a great deal of overlap in these areas-given that the initial choice of a plan affects the way in which it is debugged and that the way in which debugged plans are stored affects the way in which they are chosen for later use-it is important to separate them and understand how different planners handle them.…”

Section: A New Theory Of Planningmentioning

confidence: 99%

Case‐Based Planning: A Framework for Planning from Experience

Hammond

1990

Cognitive Science

155

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Section: A New Theory Of Planningmentioning

confidence: 99%

Case‐Based Planning: A Framework for Planning from Experience

Hammond

1990

Cognitive Science

155

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“…Asbru is an task-specific, time-oriented and intention-based plan representation language designed to embody CPG's and protocols as skeletal plans [28,29]. Each of these skeletal plans corresponds to a possible step in the guideline, and consists of a plan-body that can be composed of either subplans (e.g., a set of steps performed in parallel or sequentially), a cyclical-plan (repeated several times), plan-activations (a call to another plan), or a user-performed step (a specific action performed by the user).…”

Section: Cig Languages: Asbrumentioning

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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“…Skeletal plans are plan schemata at various levels of detail that capture the essence of the procedure, but leave room for execution-time flexibility in the achievement of particular (Friedland and Iwasaki 1985). Thus, they are usually reusable in different contexts.…”

Section: Nhohwdo 3odqvmentioning

confidence: 99%

Time-oriented skeletal plans: Support to design and execution

Miksch

Shaḥar

Horn

et al. 1997

Recent Advances in AI Planning

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$EVWUDFW Skeletal plans are a powerful way to reuse existing domain-specific procedural knowledge. In the $VJDDUG project, a set of tasks that support the design and the execution of skeletal plans by a human executing agent other than the original plan designer are designed. The underlying requirement to develop task-specific problem-solving methods is a PRGHOLQJ ODQJXDJe. Therefore, within the Asgaard project, a time-oriented, intention-based language, called $VEUX, was developed. During the design phase of plans, Asbru allows to express durative actions and plans caused by durative states of an observed agent. The intentions underlying these plans are represented explicitly as temporal patterns to be maintained, achieved or avoided. We will present the underlying idea of the Asgaard project and explain the time-oriented Asbru language. Finally, we show the benefits and limitations of the time-oriented, skeletal plan representation to be applicable in real-world, high-frequency domains. 0RWLYDWLRQ DQG ,QWURGXFWLRQWe are motivated by the need for knowledge-based support in the medical domain. Health care providers are faced with two problems: (1) the information overload resulting from modern equipment, and (2) improving the quality of health care through increased awareness of proper disease management techniques. &OLQLFDO SURWRFROV and JXLGHOLQHV should solve the difficulties. Clinical guideline refers to a general principle by which a course of actions is determined and clinical protocol refers to a general class of therapeutic interventions. In the following, we will use clinical guideline and protocol interchangeable.Appropriate clinical protocols are only available for a very limited class of clinical problems. They are not adjusted to the patient data-management system, they are partly vague and incomplete concerning their intentions and their temporal and context-dependent representation, and most often they are outdated after being developed. Extracting and formulating the knowledge structure for clinical protocols is a non trivial task. The context implicit in the protocols must be made explicit.

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The concept and implementation of skeletal plans

Cited by 131 publications

References 21 publications

Case‐Based Planning: A Framework for Planning from Experience

Case‐Based Planning: A Framework for Planning from Experience

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

Time-oriented skeletal plans: Support to design and execution

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