35,240 research outputs found
SmartPM: automatic adaptation of dynamic processes at run-time
The research activity outlined in this thesis is devoted to define a general approach, a concrete architecture and a prototype Process Management System (PMS) for the automated adaptation of dynamic processes at run-time, on the basis of a declarative specification of process tasks and relying on well-established reasoning about actions and planning techniques. The purpose is to demonstrate that the combination of procedural and imperative models with declarative elements, along with the exploitation of techniques from the field of artificial intelligence (AI), such as Situation Calculus, IndiGolog and automated planning, can increase the ability of existing PMSs of supporting dynamic processes. To this end, a prototype PMS named SmartPM, which is specifically tailored for supporting collaborative work of process participants during pervasive scenarios, has been developed. The adaptation mechanism deployed on SmartPM is based on execution monitoring for detecting failures at run-time, which does not require the definition of the adaptation strategy in the process itself (as most of the current approaches do), and on automatic planning techniques for the synthesis of the recovery procedure
Towards The Integration of Model Predictive Control into an AI Planning Framework
This paper describes a framework for a hybrid algorithm that combines both AI Planning and Model Predictive Control approaches to reason with processes and events within a domain. This effectively utilises the strengths of search-based and model-simulation-based methods. We explore this control approach and show how it can be embedded into existing, modern AI Planning technology. This preserves the many advantages of the AI Planning approach, to do with domain independence through declarative modelling, and explicit reasoning, while leveraging the capability of MPC to deal with continuous processes computation within such domains. The developed technique is tested on an urban traffic control application and the results demonstrate the
potential in utilising MPC as a heuristic to guide planning search
VERTO: a visual notation for declarative process models
Declarative approaches to business process modeling allow to represent loosely-structured
(declarative) processes in flexible scenarios as a set of constraints on the allowed flow of
activities. However, current graphical notations for declarative processes are difficult to
interpret. As a consequence, this has affected widespread usage of such notations, by
increasing the dependency on experts to understand their semantics. In this paper, we
tackle this issue by introducing a novel visual declarative notation targeted to a more
understandable modeling of declarative processes
OptBPPlanner: Automatic Generation of Optimized Business Process Enactment Plans
Unlike imperative models, the specifi cation of business process (BP)
properties in a declarative way allows the user to specify what has to be done instead
of having to specify how it has to be done, thereby facilitating the human work
involved, avoiding failures, and obtaining a better optimization. Frequently, there
are several enactment plans related to a specifi c declarative model, each one
presenting specifi c values for different objective functions, e.g., overall completion
time. As a major contribution of this work, we propose a method for the automatic
generation of optimized BP enactment plans from declarative specifi cations. The
proposed method is based on a constraint-based approach for planning and scheduling
the BP activities. These optimized plans can then be used for different purposes
like simulation, time prediction, recommendations, and generation of optimized BP
models. Moreover, a tool-supported method, called OptBPPlanner, has been implemented
to demonstrate the feasibility of our approach. Furthermore, the proposed
method is validated through a range of test models of varying complexity.Ministerio de Ciencia e Innovación TIN2009-1371
Supporting the Optimized Execution of Business Processes through Recommendations
In order to be able to flexibly adjust a company’s business
processes (BPs) there is an increasing interest in flexible Process-Aware
Information Systems (PAISs). This increasing flexibility, however, typically
implies decreased user guidance by the PAIS and thus poses additional
challenges to its users. This work proposes a recommendation
system which assists users during process execution to optimize performance
goals of the processes. The recommendation system is based on a
constraint-based approach for planning and scheduling the BP activities
and considers both the control-flow and the resource perspective.Ministerio de Ciencia e Innovación TIN2009-1371
Optimized Time Management for Declarative Workflows
Declarative process models are increasingly used since they fit better
with the nature of flexible process-aware information systems and the requirements
of the stakeholders involved. When managing business processes, in addition,
support for representing time and reasoning about it becomes crucial. Given
a declarative process model, users may choose among different ways to execute
it, i.e., there exist numerous possible enactment plans, each one presenting specific
values for the given objective functions (e.g., overall completion time). This
paper suggests a method for generating optimized enactment plans (e.g., plans
minimizing overall completion time) from declarative process models with explicit
temporal constraints. The latter covers a number of well-known workflow
time patterns. The generated plans can be used for different purposes like providing
personal schedules to users, facilitating early detection of critical situations,
or predicting execution times for process activities. The proposed approach is
applied to a range of test models of varying complexity. Although the optimization
of process execution is a highly constrained problem, results indicate that
our approach produces a satisfactory number of suitable solutions, i.e., solutions
optimal in many cases
Instructional strategies and tactics for the design of introductory computer programming courses in high school
This article offers an examination of instructional strategies and tactics for the design of introductory computer programming courses in high school. We distinguish the Expert, Spiral and Reading approach as groups of instructional strategies that mainly differ in their general design plan to control students' processing load. In order, they emphasize topdown program design, incremental learning, and program modification and amplification. In contrast, tactics are specific design plans that prescribe methods to reach desired learning outcomes under given circumstances. Based on ACT* (Anderson, 1983) and relevant research, we distinguish between declarative and procedural instruction and present six tactics which can be used both to design courses and to evaluate strategies. Three tactics for declarative instruction involve concrete computer models, programming plans and design diagrams; three tactics for procedural instruction involve worked-out examples, practice of basic cognitive skills and task variation. In our evaluation of groups of instructional strategies, the Reading approach has been found to be superior to the Expert and Spiral approaches
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