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Preface
Planning and Scheduling is the field of Artificial Intelligence that is concerned with all aspects of
the system-supported or fully automated synthesis, execution, and monitoring of courses of actions,
activities, and tasks. With that, it provides a technology for increasing the autonomy of systems by
making them more flexible, robust, and...
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Citations
... KF as well as knowledge-based planning prompt related ideas regarding knowledge embedding while developing models. Knowledge-based planning is defined as a method of planning engaged for identifying an order of tasks done by many agents under given existing conditions and resource constrains for attaining ultimate goals [51]. The method comprises of acquiring knowledge, validating it, planning domains' knowledge maintenance, adopting proper knowledge oriented planning tools for developing planning models [51]. ...
... Knowledge-based planning is defined as a method of planning engaged for identifying an order of tasks done by many agents under given existing conditions and resource constrains for attaining ultimate goals [51]. The method comprises of acquiring knowledge, validating it, planning domains' knowledge maintenance, adopting proper knowledge oriented planning tools for developing planning models [51]. For instance, R-Moreno et al. [52] used a planning and scheduling platform and a work flow modeling tool in order to plan a model of telephone installation workflow. ...
Flow of Knowledge pertains in most teamwork and it attracts the researchers in the field of knowledge management. Since flow of knowledge usually happens in a particular context, context has to be taken into consideration in knowledge flow modeling. Past models of flow of knowledge lacked in terms of software development and lacked in-depth exploration of context modeling, which made those models less applicable. This research proposes a conceptual framework of context oriented flow of knowledge in software development aspects. In this framework, the context is seen as an inseparable element of flow of knowledge, which is regarding the creation, transformation as well as application of knowledge items. Framework KFFD is proposed based on three aspects- flow of knowledge, software development project and ontology. For the proper use of knowledge, dynamic flow to destinations form sources is necessary. In this context, a new framework in KM is analyzing, designing and implementing knowledge flow management systems. One crucial challenge in such systems is the exploration of flow of knowledge from its origin to the receiver. Another challenge is controlling the flow for quality enhancements regarding the requirements of the users. As such, the aim of this research is to provide a framework so as to solve this challenge.
... Para especificar una tarea en PDDL se identifican dos archivos, el primero define el dominio que describe los predicados y las posibles acciones, el segundo detalla el problema donde se describen los objetos, el punto inicial y la meta. [15] A partir de [13] y [14] se realizó una primera aproximación en [16] y específicamente en este trabajo usamos otras técnicas de PLN y almacenamos lo extraído en una ontología. El uso de ontologías como BC mejora lo propuesto en [13], [14] y [16], ya que se consigue un gran avance en los procesos de adquisición y gestión del conocimiento asociado a los dominios de planificación, problemática que es tratada por [17]: la comunidad de planificación debe estudiar las técnicas y herramientas relacionadas con la gestión de conocimiento desarrolladas en otras áreas, y estudiar cómo adaptarlas e integrarlas en los sistemas de planificación, además se pueden generar nuevos planes por recombinación aprovechando el conocimiento almacenado, así como también poseer un vocabulario expresivo con el que se puedan definir nuevos problemas de planificación planteando nuevos estados y acciones [18]. ...
Automated Planning (AP) is the discipline of Artificial Intelligence whose aim is to produce a sequence of actions in order to achieve a specific goal and requires the inflow of a pre-defined action model. However, to start the construction of this model is a difficult task even for experts. This paper proposes the extraction of information from the Web, in order to identify those elements that correspond to the action model for AP tasks, by analyzing a number of Web pages that contain data of already existing programs and extracting from them a set of tools which would allow to solve any given problem. The data obtained will be processed using Natural Language Processing (NLP) tools, in order to identify a set of actions that are part of an AP domain model. The proposed system reached an average accuracy of89.87% and save all actions taken on ontology to form a Knowledge Base (BC), which allows later use for other domains of PA. This article presents the results of a partial research about the uses of extraction tools, pre-processing, identification of components and storage in the ontology.
... For example, it was mentioned more than a decade ago by Jonathan et al. (1999). It is also discussed in the 2003 roadmap of the PLANET Network of Excellence in AI Planning (Biundo et al., 2003 ). More recently, Moreno et al. (2007) implemented the idea in the SHAMASH system. ...
Planning is concerned with the automated solution of action sequencing problems described in declarative languages giving the action preconditions and e ects. One important application area for such technology is the creation of new processes in Business Process Management (BPM), which is essential in an ever more dynamic business environment. A major obstacle for the application of Planning in this area lies in the modeling. Obtaining a suitable model to plan with { ideally a description in PDDL, the most commonly used planning language { is often prohibitively complicated and/or costly. Our core observation in this work is that this problem can be ameliorated by leveraging synergies with model-based software development. Our application at SAP, one of the leading vendors of enterprise software, demonstrates that even one-to-one model re-use is possible. The model in question is called Status and Action Management (SAM). It describes the behavior of Business Objects (BO), i.e., large-scale data structures, at a level of abstraction corresponding to the language of business experts. SAM covers more than 400 kinds of BOs, each of which is described in terms of a set of status variables and how their values are required for, and a ected by, processing steps (actions) that are atomic from a business perspective. SAM was developed by SAP as part of a major model-based software engineering e ort. We show herein that one can use this same model for planning, thus obtaining a BPM planning application that incurs no modeling overhead at all. We compile SAM into a variant of PDDL, and adapt an o -the-shelf planner to solve this kind of problem. Thanks to the resulting technology, business experts may create new processes simply by specifying the desired behavior in terms of status variable value changes: e ectively, by describing the process in their own language.
... The application of planning to service composition has first appeared as an idea in the late 1990s (e.g., [13]) and has been intensively researched since the early 2000s (e.g., [54, 64, 19, 44, 63, 48, 37, 40]). The different approaches differ widely in intention and scope, as well as underlying formalisms (see some details in Section 6.4.5 below). ...
The research area of Semantic Web Services investigates the annotation of services, typically in a SOA, with a precise mathematical meaning in a formal ontology. These annotations allow a higher degree of automation. The last decade has seen a wide proliferation of such approaches, proposing different ontology languages, and paradigms for employing these in practice. The next chapter gives an overview of these approaches. In the present chapter, we provide an understanding of the fundamental techniques, from Artificial Intelligence and Databases, on which they are built. We give a concise, ontology-language independent, overview of the techniques most frequently used to automate service discovery and composition.
... En 2003, la European Network of Excellence in AI Planning definió la Ingeniería del Conocimiento en Planificación ([17], capítulo 7) como el proceso que comprende:1. la adquisición, validación, verificación y mantenimiento de los modelos de dominios de planificación; 2. y la selección de los mecanismos de planificación adecuados y su integración con el modelo del dominio para construir una aplicación.En el citado documento se reconoce que el área de planificación tiene características únicas que la diferencian de otras áreas en las que también se estudian sistemas basados en conocimiento. ...
... This approach in itself is not particularly novel; even the application of Planning in the BPM context has been foreseen long ago already ( Biundo et al. 2003). The exciting bit is that, at SAP, there is a near-perfect answer to the question that was left un-answered in pretty much every previous work we are aware of: How to get the model? ...
Business processes control the flow of activities within and between enterprises. Business Process Management is concerned, amongst other things, with the maintenance of these processes. In particular, it becomes ever more important to be able to quickly create modified processes for changed market conditions. We show that AI Planning can help with this, by automatically composing process skeletons. We formalize this as a particular form of planning with non-deterministic actions. Since there is no fixed ``domain'' -- business processes may talk about almost anything -- a major problem in applying the method is a practical way of obtaining the planning model. We show that, at SAP, one of the leading providers of enterprise software, one can obtain the models for free, by leveraging existing semi-formal models of software behavior. We finally show that, by arranging some known planning techniques in a suitable way, one can obtain tooling that solves practical examples in a matter of seconds, and that is hence suitable for use in a real-time BPM process modeling environment. Our prototype of such an environment is part of a research extension to the SAP NetWeaver platform.
... Artificial Intelligence Planning techniques have shown very useful in the resolution of different problems related to logistics and workflow domains, just to cite a few (Biundo et al., 2003). However, many real world problems require the inclusion of an implicit representation and handling of time for the plans to be correctly executed so that some scheduling techniques must be imported into a planning framework. ...
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.
... However, applications of automated planning can also take advantage of the more recent developments that alleviate the 'knowledge bottleneck': the development of shared ontologies and globally accessible knowledge, and the development of standard, tool support environments for the engineering of knowledge. For a discussion of the similarities and distinguishing features between knowledge engineering for AI planning and KBS, the reader is referred to section 7 of PLANET's Roadmap (Biundo et al. 2003). ...
In this paper we address the problem of deciding whether it is feasible to apply AI planning technology (involving currently available planning engines) to an application area. We develop some criteria based on motivation, technological infrastructure and knowledge engineering aspects of an application, and we go on to apply these criteria to two application areas. The cri- teria both help to evaluate the overall feasibility, and in cases where development continues, help us to focus on the parts of the application which are likely to be most troublesome. from AI planning technology, and how can we determine what areas of the application would cause the most prob- lems? In this paper we explore the characteristics of an application area that make the application of AI plan- ning feasible. To motivate the discussion, we use two particular applications from the Transport and Water Management service industries respectively. These are wide ranging, complex, involve many stake holders and organisations, and have allied research and development areas. This endeavour has much in common with the general area of business process change through the introduc- tion of new technology, and in particular the introduc- tion of knowledged-based AI technology. The potential problem areas in the application of automated plan- ning are in some cases similar to challenges already well known when implementing KBS systems. These include the 'knowledge bottleneck' - the diculty of
In knowledge-based organizations, workers need task-relevant knowledge and documents to support their task performance. A knowledge flow (KF) represents the flow of an individual’s or group members’ knowledge-needs and the referencing sequence of documents in the performance of tasks. Through knowledge flows, organizations can provide task-relevant knowledge to workers to fulfill their knowledge-needs. Nevertheless, in a collaborative environment, workers usually have different knowledge-needs in accordance with their individual task functions. Conventional KF models do not provide workers with the different views of a knowledge flow that they require to meet these knowledge-needs. Several researchers have investigated KF models but they did not address the concept of the knowledge-flow view (KFV).This study proposes a theoretical model of the KFV using innovative methods. Basically, a KFV is a virtual knowledge flow derived from a base knowledge flow that abstracts knowledge concepts for individual workers based on their knowledge-needs. The KFV model in this study builds knowledge-flow views by abstracting knowledge nodes in a base knowledge flow to generate corresponding virtual knowledge nodes through an order-preserving approach and a knowledge concept generalization mechanism. The knowledge-flow views not only fulfill workers’ different knowledge-needs but also facilitate knowledge support in teamwork.
