Juan Fernández-Olivares

From business process models to hierarchical task network planning domains

Hierarchical Task Network (HTN) planning paradigm has been widely used during the last decade to model and solve planning and scheduling (P&S) problems, and it has proved to be very useful in the planning and coordination of human tasks. At the same time, Business Process Management (BPM) tools are being increasingly used in the modeling of organizations’ business practices and processes, but their life cycle has shown to have some shortages (as the possibility to obtain context-dependent plan instances). In this paper we present a methodology and software framework to translate Business Process Models into HTN P&S domains, in order to cover some of these deficiencies. 1 Introduction and motivation Enterprises and organizations are facing today the emerging challenge of integration and automation of their business processes. The complexity of this issue increases when they have to deal with human-centric processes, as they are usually carried out in an informal manner, and the coordination of the different tasks and participants involved in these processes is very difficult to achieve. In this case, new technologies, mostly oriented to support decision making, have to be introduced to help knowledge workers like organization managers and decision makers to successfully achieve this goal.

Automatic Generation of User Adapted Learning Designs: An AI-Planning Proposal

A Learning Design(LD) definition under the IMS-LD standard is a complex task for the instructor because it requires a lot of time, effort and previous knowledge of the students group over which will be defined the knowledge objectives. That is why, taking advantage from diffusion of learning objects(LO) labeling using IMS-MD standard, we have proposed to realize a knowledge engineering process, represented as an algorithm, over LO labels and user profiles to automaticaly define a domain that will be used by an intelligent planner to build a LD. This LD will be finally implemented in the ILIAS Learning Management System(LMS).

A Middle-Ware for the Automated Composition and Invocation of Semantic Web Services Based on Temporal HTN Planning Techniques

This work presents a middle-ware able to translate OWL-S web services descriptions into a temporal HTN domain in order to automatically compose and execute sequences of web service invocations, including parallel branches and complex synchronizations, based on the combination of HTN planning and temporal reasoning techniques.

Handling fuzzy temporal constraints in a planning environment

Abstract An interleaved integration of the planning and scheduling process is presented with the idea of including soft temporal constraints in a partial order planner that is being used as the core module of an intelligent decision support system for the design forest fire fighting plans. These soft temporal constraints have been defined through fuzzy sets. This representation allows us a flexible representation and handling of temporal information. The scheduler model consists of a fuzzy temporal constraints network whose main goal is the consistency checking of the network associated to each partial order plan. Moreover, we present a model of estimating this consistency, and show the monitoring and rescheduling capabilities of the system. The resulting approach is able to tackle problems with ill defined knowledge, to obtain plans that are approximately consistent and to adapt the execution of plans to unexpected delays.

Knowledge Engineering and Planning for the Automated Synthesis of Customized Learning Designs

This paper describes an approach to automatically obtain an HTN planning domain from a well structured learning objects repository and also to apply an HTN planner to obtain IMS Learning Designs adapted to the features and needs of every student.

Context-Aware generation and adaptive execution of daily living care pathways

Ambient Intelligence (AmI) can be of great support for the care of people with cognitive impairment. This people require effective treatment plans with clear goals, including pharmacological treatment, Activities of Daily Living (ADL) and assessment tests, all of them according to the recommendations specified in Clinical Practice Guidelines (CPGs) and centred on the patient. Moreover, these plans need to be adapted in a sensitive and responsive way to both the natural disease evolution and unexpected circumstances. This work presents an approach to automatically generate (from formal CPGs) and adaptively execute daily living care plans in the frame of a planning-based distributed architecture that allows for its application on AmI environments. This approach is based on temporal hierarchical planning and scheduling techniques, which allow for the context-awareness of the whole process.

Planning for Conditional Learning Routes

This paper builds on a previous work in which an HTN planner is used to obtain learning routes expressed in the standard language IMS-LD and its main contribution is the extension of a knowledge engineering process that allows us to obtain conditional learning routes able to adapt to run time events, such as intermediate course evaluations, what is known as the standard IMS-LD level B.

A knowledge-based architecture for the management of patient-focused care pathways

There is need for software systems in order to coordinate the activities of healthcare professionals involved in the treatment of a patient, aligning the care delivery around already existing Clinical Practice Guidelines (CPGs). This is being carried out in care organizations by implementing integrated Care Pathways (CPs). Nonetheless, the generation of these care pathways is not trivial, and multiple barriers exist for their development and enactment. In this paper, a knowledge-based architecture is presented that, by means of Knowledge Engineering methods and Artificial Intelligence Planning and Scheduling (AI P&S) techniques, is able to automatically generate these care pathways from a computer-interpretable representation of CPGs, tackling some of these barriers. Firstly, these techniques consider the patient profile, the care organization details as well as the temporal and resource constraints, implicit in a care process, in order to generate a patient-focused care pathway. Moreover, they also allow the enactment of personalized care plans in a web-based format, powered by a workflow runtime engine, thus providing an ubiquitous and interactive execution to healthcare professionals. Finally, the architecture also includes monitoring and replanning techniques in order to check the current health status of patients and adapt care plans when they do not progress as expected. For the experimental evaluation of the architecture, several tests have been carried out in order to simulate a clinical environment where different care plans were automatically executed, monitored and adapted regarding the health conditions of patients as well as the recommendations specified in a real, CPG of the paediatric oncology area. As conclusion, the proposed architecture seems to be an adequate infrastructure for supporting the automated generation as well as the interactive execution and monitoring of patient-focused care pathways.

Workflow Planning for E-Learning Center Management

Workflow technology was developed in order to help to understand, coordinate and orchestrate the processes that make an organisation work. It is perfectly suitable for the management of an e-learning center, where a course manager have to allocate the execution of a set of tasks among the existing human resources. This paper presents how to make the most of an e-learning business model stored in XPDL, converting it into a planning representation, using the HTN paradigm. The subsequent goal is to obtain an action plan that distribute the tasks needed for the creation and configuration of an online course to a set of workers.

Time prediction on multi-perspective declarative business processes

Process-aware information systems (PAISs) are increasingly used to provide flexible support for business processes. The support given through a PAIS is greatly enhanced when it is able to provide accurate time predictions which is typically a very challenging task. Predictions should be (1) multi-dimensional and (2) not based on a single process instance. Furthermore, the prediction system should be able to (3) adapt to changing circumstances and (4) deal with multi-perspective declarative languages (e.g., models which consider time, resource, data and control flow perspectives). In this work, a novel approach for generating time predictions considering the aforementioned characteristics is proposed. For this, first, a multi-perspective constraint-based language is used to model the scenario. Thereafter, an optimized enactment plan (representing a potential execution alternative) is generated from such a model considering the current execution state of the process instances. Finally, predictions are performed by evaluating a desired function over this enactment plan. To evaluate the applicability of our approach in practical settings we apply it to a real process scenario. Despite the high complexity of the considered problems, results indicate that our approach produces a satisfactory number of good predictions in a reasonable time.