In this paper we propose an architecture for an adaptive workflow management system (WFMS) and present the research prototype CBRFlow. CBRFlow extends workflow execution with conversational case-based reasoning (CCBR) to adapt the predefined workflow model to changing circumstances and to provide the WFMS with learning capabilities. Business rules within the predefined workflow model are annotated during run-time with context-specific information in the form of cases using the CCBR sub-system. When case reuse becomes frequent, the cases are manually refactored into rules to foster automatic execution. This feedback supports continuous process improvement, resulting in more manageable and more efficient business processes over time.

CBRFlow: Enabling Adaptive Workflow Management Through Conversational Case-Based Reasoning

https://pure.rug.nl/ws/files/83768215/HTN_planning_Overview_comparison_and_beyond.pdf

HTN planning

Process-aware information systems (PAIS) allow coordinating the execution of business processes by providing the right tasks to the right people at the right time. In order to support a broad spectrum of business processes, PAIS must be flexible at run-time. Ad-hoc deviations from the predefined process schema as well as the quick adaptation of the process schema itself due to changes of the underlying business processes must be supported. This paper presents an integrated approach combining the concepts and methods provided by the process management systems ADEPT and CBRFlow. Integrating these two systems enables ad-hoc modifications of single process instances, the memorization of these modifications using conversational case-based reasoning, and their reuse in similar future situations. In addition, potential process type changes can be derived from cases when similar ad-hoc modifications at the process instance level occur frequently.

/pdf/ccbr-driven-business-process-evolution-mt7gurleh2.pdf

CCBR–Driven business process evolution

Automating the development of construction schedules has been an interesting topic for researchers around the world for almost three decades. Researchers have approached solving scheduling problems with different tools and techniques. Whenever a new artificial intelligence or op- timization tool has been introduced, researchers in the con- struction field have tried to use it to find the answer to one of their key problems—the "better" construction schedule. Each researcher defines this "better" slightly different. This article reviews the research on automation in construction scheduling from 1985 to 2014. It also covers the topic using different approaches, including case-based reasoning, knowledge-based approaches, model-based approaches, ge- netic algorithms, expert systems, neural networks, and other methods. The synthesis of the results highlights the share of the aforementioned methods in tackling the scheduling chal- lenge, with genetic algorithms shown to be the most dominant approach. Although the synthesis reveals the high applicabil- ity of genetic algorithmstothe different aspects ofmanaginga project, including schedule, cost, and quality, it exposed a more limited project management application for the other methods.

Automation in construction scheduling: a review of the literature

Shop is a hierarchical task network planning algorithm that is provably sound and complete across a large class of planning domains. It plans for tasks in the same order that they will later be executed, and thus, it knows the current world state at each step of the planning process. shop takes advantage of this knowledge by allowing a high degree of expressive power in its knowledge bases. For example, shop's preconditions can include logical inferences, complex numeric computations, and calls to external programs. shop is powerful enough that an implementation of it is being used as an embedded planner in the Naval Research Laboratory's hicap system.

/pdf/the-shop-planning-system-mr68jy1iur.pdf

The Shop Planning System

Project management is a business process for successfully delivering one-of-a kind products and services under real-world time and resource constraints. Developing a project plan, a crucial element of project management, is a difficult task that requires significant experience and expertise. Interestingly, artificial intelligence researchers have developed both mixed-initiative and automated hierarchical planning systems for reducing planning effort and increasing plan evaluation measures. However, they have thus far not been used in project planning, in part because the relationship between project planning and hierarchical planning has not been established, hi this chapter, we identify this relationship and explain how project planning representations called work breakdown structures (WBS) are similar to plan representations employed by hierarchical planners. We exploit this similarity and apply well-known hierarchical planning techniques, including an integrated (case-based) plan retrieval module, to assist a project planner efficiently create WBSs. Our approach uses stored episodes (i.e., cases) of previous project planning experiences to support the development of new plans. We present an architecture for knowledge-based project planning system that implements this approach.

/pdf/knowledge-based-project-planning-3q1ips9m55.pdf

Kalyan Gupta

Papers

Knowledge-Based Project Planning