Rosina O. Weber

Intelligent lessons learned systems

Lessons learned processes have been deployed in commercial, government, and military organizations since the late 1980s to capture, store, disseminate, and share experiential working knowledge. However, recent studies have shown that software systems for supporting lesson dissemination do not effectively promote knowledge sharing. We found that the problems with these systems are related to their textual representation for lessons and that they are not incorporated into the processes they are intended to support. In this article, we survey lessons learned processes and systems, detail their capabilities and limitations, examine lessons learned system design issues, and identify how artificial intelligence technologies can contribute to knowledge management solutions for these systems.

Textual case-based reasoning

This commentary provides a definition of textual case-based reasoning (TCBR) and surveys research contributions according to four research questions. We also describe how TCBR can be distinguished from text mining and information retrieval. We conclude with potential directions for TCBR research.

Intelligent delivery of military lessons learned

Abstract Lessons learned systems (LLS) are a common knowledge management (KM) initiative among the American government agencies (e.g., Department of Defense (DOD), Department of Energy (DOE), NASA). An effective lessons learned (LL) process can substantially improve decision processes, thus representing an essential chapter in a knowledge-sharing digital government. Unfortunately, these systems typically fail to deliver lessons when and where they are needed. In this paper, we introduce, describe, and empirically evaluate the monitored distribution (MD) approach for the active delivery of lessons learned. Our results show that this just-in-time information delivery approach, embedded in a decision support system (DSS) for plan authoring, significantly improved plan execution performance measures.

Knowledge management in case-based reasoning

This commentary describes two core knowledge management approaches that applied case-based reasoning as a methodological foundation for organizational systems managing experience. These research projects illustrate the presence of knowledge management in case-based reasoning by focusing on the dualism between case-based reasoning and organizational approaches targeting knowledge management goals.

Investigating graphs in textual case-based reasoning

Textual case-based reasoning (TCBR) provides the ability to reason with domain-specific knowledge when experiences exist in text. Ideally, we would like to find an inexpensive way to automatically, efficiently, and accurately represent textual documents as cases. One of the challenges, however, is that current automated methods that manipulate text are not always useful because they are either expensive (based on natural language processing) or they do not take into account word order and negation (based on statistics) when interpreting textual sources. Recently, Schenker et al. [1] introduced an algorithm to convert textual documents into graphs that conserves and conveys the order and structure of the source text in the graph representation. Unfortunately, the resulting graphs cannot be used as cases because they do not take domain knowledge into consideration. Thus, the goal of this study is to investigate the potential benefit, if any, of this new algorithm to TCBR. For this purpose, we conducted an experiment to evaluate variations of the algorithm for TCBR. We discuss the potential contribution of this algorithm to existing TCBR approaches.

Active Delivery for Lessons Learned Systems

Lessons learned processes, and software systems that support them, have been developed by many organizations (e.g., all USA military branches, NASA, several Department of Energy organizations, the Construction Industry Institute). Their purpose is to promote the dissemination of knowledge gained from the experiences of an organizations employees. Unfortunately, lessons learned systems are usually ineffective because they invariably introduce new processes when, instead, they should be embedded into the processes that they are meant to improve. We developed an embedded case-based approach for lesson dissemination and reuse that brings lessons to the attention of users rather than requiring them to fetch lessons from a standalone software tool. We demonstrate this active lessons delivery architecture in the context of HICAP, a decision support tool for plan authoring. We also show the potential of active lessons delivery to increase plan quality for a new travel domain.

Nomadic context-aware knowledge management systems: applications, challenges and research problems

Increasingly, knowledge is being created and applied on the move by knowledge workers. In this paper, we discuss the concept of nomadic context-aware Knowledge Management Systems (KMS). In nomadic context-aware KMS, computing becomes inseparable from the environment and work of the knowledge worker, and they serve to support their knowledge intensive activities as they change while they are on the move. This paper introduces nomadic computing systems, and describes how they can significantly enhance the usability of one common kind of KMS, lessons learned systems. We examine potential enhancements for KMS, and identify some of the challenges involved in the design and deployment of nomadic context-aware KMS. We define some directions for future research and discuss how existing methods can bring us closer to nomadic context-aware KMS.

Predicting software development project outcomes

Case-based reasoning is a flexible methodology to manage software development related tasks. However, when the reasoners task is prediction, there are a number of different CBR techniques that could be chosen to address the characteristics of a dataset. We examine several of these techniques to assess their accuracy in predicting software development project outcomes (i.e., whether the project is a success or failure) and identify critical success factors within our data. We collected the data from software developers who answered a questionnaire targeting a software development project they had recently worked on. The questionnaire addresses both technical and managerial features of software development projects. The results of these evaluations are compared with results from logistic regression analysis, which serves as a comparative baseline. The research in this paper can guide design decisions in future CBR implementations to predict the outcome of projects described with managerial factors.

Soft case-based reasoning

The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.

Knowledge management for computational intelligence systems

Computer systems do not learn from previous experiences unless they are designed for this purpose. Computational intelligence systems (CIS) are inherently capable of dealing with imprecise contexts, creating a new solution in each new execution. Therefore, every execution of a CIS is valuable to be learned. We describe an architecture for designing CIS that includes a knowledge management (KM) framework, allowing the system to learn from its own experiences, and those learned in external contexts. This framework makes the system flexible and adaptable so it evolves, guaranteeing high levels of reliability when performing in a dynamic world. This KM framework is being incorporated into the computational intelligence tool for software testing at National Institute for Systems Test and Productivity. This paper introduces the framework describing the two underlying methodologies it uses, i.e. case-based reasoning and monitored distribution; it also details the motivation and requirements for incorporating the framework into CIS.