From business process models to hierarchical task network planning domains

González-Ferrer, Arturo; Fernández-Olivares, Juan; Castillo, Luis

doi:10.1017/s0269888912000410

Cited by 15 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That work made us aware of the di culties inherent in using traditional planning languages, even for expert knowledge engineers. In addition, we also presented in [22] a method for automatically generating HTN planning domain and problem specifications from process models modeled by means of the well-known business process modeling notation (BPMN). However, that method did not address how to represent and translate temporal information, given the poor support provided by the BPMN standard for the representation of complex temporal patterns [23].…”

Section: Objectivementioning

confidence: 99%

“…The representation of these workflow patterns with HPDL has been already studied in [18], where, through a proof of concept, it was shown that these workflow patterns can be used to represent clinical protocols in HPDL [20]. Moreover, the translation of these workflow patterns from business process models to HPDL has been described in [22]. Therefore, the analysis carried out in the following subsections is specifically focused on time annotations, synchronization or delays between tasks, and also on repetitive or cyclical temporal patterns.…”

Section: Analysis Of Temporal Knowledgementioning

confidence: 99%

“…These tools are closer to the end users, and can represent most workflow time patterns [59]. Therefore, it would be interesting to start the translation proposed in this paper from any of these high-level notations instead of from a CIG language (as we did in [22]). …”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Objectivementioning

confidence: 99%

Section: Analysis Of Temporal Knowledgementioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The representation with HPDL of Asbru's multiple task ordering schemas (parallel, sequential, unordered or any-order), commonly known as basic workflow patterns [15], has been already studied in [4], and also for the translation of business process models in [9]. Therefore, our analysis is focused specifically on time annotations, synchronization or delays among tasks and repetitive or cyclical temporal patterns.…”

Section: Analysis Of Temporal Knowledgementioning

confidence: 99%

Careflow Planning: From Time-Annotated Clinical Guidelines to Temporal Hierarchical Task Networks

González-Ferrer

Teije

Fdez-Olivares

et al. 2011

Artificial Intelligence in Medicine

Self Cite

View full text Add to dashboard Cite

Abstract. Decision-making, care planning and adaptation of treatment are important aspects of the work of clinicians, that can clearly benefit from IT support. Clinical Practice Guidelines (CPG) languages provide formalisms for specifying knowledge related to such tasks, such as decision criteria and time-oriented aspects of the patient treatment. In these CPG languages, little research has been directed to efficiently deal with the integration of temporal and resource constraints, for the purpose of generating patient tailored treatment plans, i.e. care pathways. This paper presents an AI-based knowledge engineering methodology to develop, model, and operationalize care pathways, providing computeraided support for the planning, visualization and execution of the patient treatment. This is achieved by translating time-annotated Asbru CPG's into temporal HTN planning domains. The proposed methodology is illustrated through a case study based on Hodgkin's disease.

show abstract

“…Along the way the authors introduce PMML2PDDL, a tool for translating the data mining models written in PMML (Predictive Model Markup Language) to PDDL. > The contribution of Arturo Gonza´lez-Ferrer, Juan Ferna´ndez-Olivares, and Luis Castillo lies in developing a translator to create planning domain models from Business Process Management Notation (BPMN; Gonza´lez-Ferrer et al, 2013). Their system provides a tool for analysts that need to perform resource allocation analysis on business workflows.…”

mentioning

confidence: 99%

Preface to special issue on Knowledge Engineering for Planning and Scheduling

Barták

Fratini

McCluskey

2013

The Knowledge Engineering Review

View full text Add to dashboard Cite

The main focus of Planning and Scheduling (P&S) research is centred around the construction of solver engines, which accept a domain (and task) model as input, and output solutions to P&S problems. This research is beginning to lead to the widespread use of the technology, and P&S solvers can be found embedded in a number of application areas, including control of UAVs, story narrative generation, Web service composition, and logistics. The domain models for these applications are created to contain knowledge such as the physics of the actions, the objects affected by the actions, and the goals or tasks that require solving. The focus on the solver engine needs to be complemented with research on the construction, validation, and optimisation of domain models and domain model languages, such as PDDL (Planning Domain Definition Language). Interest in knowledge engineering (KE) for P&S therefore has grown in recent years, and is concentrated on the formulation of application knowledge bases and the fusion of them with planning engines to create operational systems. Although the main aspect of KE is in domain modelling, it also includes the areas of heuristic acquisition, planner-domain matching, domain knowledge validation, and so forth.We can categorise methods of producing domain models containing representations of actions, ready for use in a solver, into two main areas:1. Handcrafted method: a user builds up a model from scratch, perhaps aided with the use of knowledge acquisition tools. In this case, the user needs to be an expert in the domain model language that the solver accepts. 2. Automated method: a user creates or obtains a model in an application-oriented language, and a translator or learning algorithm inputs the model in the application language and outputs solver-ready domain models. The input model could range from being in the form of a declarative formal specification, to a set of example plan scripts.Over the last 40 years in the development of P&S, domain models have been produced in the main using method 1. An important aspect, relevant for the progression of the field of domain independent P&S, is that general solver engines can be accessed and used by non-AI (artificial intelligence) experts. The development of tools and techniques within method 2 furthers this aim, and overcomes the need to handcraft domain models, which is seen by some as a limiting factor in the deployment of P&S. This recognises that experts in that area may be familiar with their own description languages, but not with P&S description languages such as PDDL. It holds the promise that P&S solvers could be embedded into tool support in the application without the need for a planning expert.This special issue contains the selected and extended papers of some of the competitors from the third competition on KE for AI P&S systems (International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS)), held during ICAPS, the International https://www.cambridge.org/core/terms. https://doi

show abstract

From business process models to hierarchical task network planning domains

Cited by 15 publications

References 23 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

Careflow Planning: From Time-Annotated Clinical Guidelines to Temporal Hierarchical Task Networks

Preface to special issue on Knowledge Engineering for Planning and Scheduling

Contact Info

Product

Resources

About