Intelligent lessons, learned systems

Decision support systems, 2003

Lessons learned systems are a common knowledge management initiative among the American government (e.g., Department of Defense, Department of Energy, NASA). An effective lessons learned 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 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 for plan authoring, significantly improved plan execution performance measures.

2003

Lessons learned systems are knowledge management solutions that serve the purpose of capturing, storing, disseminating and sharing an organization’s verified lessons. In this paper we propose a two-step categorization method to support the design of intelligent lessons learned systems. The first step refers to the categories of the lessons learned processes the system is designed to support. The second step refers to the categories of the system itself. These categories are based on systems available online and described in the literature. We conclude by summarizing representation and other important issues that need to be addressed when designing intelligent lessons learned systems. Motivation and definition Lessons learned (LL) systems have been deployed by many military, commercial, and governmental organizations to disseminate validated experiential lessons. They support organizational lessons learned processes, which use a knowledge management (KM) approach to collect, store, d...

2000

Lessons learned systems are knowledge management solutions that serve the purpose of capturing, storing, disseminating and sharing an organization's verified lessons. In this paper we propose a two-step categorization method to support the design of intelligent lessons learned systems. The first step refers to the categories of the lessons learned processes the system is designed to support. The second step refers to the categories of the system itself. These categories are based on systems available online and described in the literature. We conclude by summarizing representational and other important issues that need to be addressed when designing intelligent lessons learned systems. Motivation and definition Lessons learned (LL) systems have been deployed by many military, commercial, and government organizations to disseminate validated experiential lessons. 1 They support organizational lessons learned processes, which use a knowledge management (KM) approach to collect, store, disseminate, and reuse experiential working knowledge that, when applied, can significantly benefit targeted organizational processes (Davenport & Prusak, 1998). Unfortunately, based on our interviews and discussions with members of several LL centers (e.g., at the Joint Warfighting Center, the Department of Energy (DOE), the Naval Facilities Engineering Command, Goddard Space Flight Center (NASA), the Construction Industry Institute), we learned that LL systems, although well-intentioned, are rarely used. Our goal is to design, develop, evaluate, and deploy LL systems that support knowledge sharing. In this paper, we categorize LL systems and identify some pertinent research directions that may benefit from applying artificial intelligence (AI) techniques. Lessons learned were originally conceived of as guidelines, tips, or checklists of what went right or wrong in a particular event (Stewart, 1997); the Canadian Army Lessons Learned Center and the Secretary of the Army for Research, Development, and Acquisition, among others, still use this notion. Today, this concept has evolved because organizations working towards improving the View publication stats View publication stats

Foundations of Intelligent …, 2010

A learned lesson, in the context of a pre-defined organizational process, summarizes an experience that should be used to modify that process, under the conditions for which that lesson applies. To promote lesson reuse, many organizations employ lessons learned processes, which define how to collect, validate, store, and disseminate lessons among their personnel, typically by using a standalone retrieval tool. However, these processes are problematic: they do not address lesson reuse effectively. We demonstrate how reuse can be facilitated through a representation that highlights reuse conditions (and other features) in the context of lessons learned systems embedded in targeted decision-making processes. We describe a case-based reasoning implementation of this concept for a decision support tool and detail an example.

Advances in case-based …, 2000

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 organization's 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.

International Joint Conference on Artificial Intelligence, 2001

Many organizations employ lessons learned (LL) processes to collect, analyze, store, and distribute, validated experiential knowledge (lessons) of their members that, when reused, can substantially improve organizational decision processes. Unfortunately, deployed LL systems do not facilitate lesson reuse and fail to bring lessons to the attention of the users when and where they are needed and applicable (i.e., they fail

2001

Department of Computer Science, University of Maryland, College Park, MD 20742 [email protected] Abstract . Knowledge management (KM) systems manipulate organizational knowledge by storing and redistributing corporate memories that are acquired from the organization’s members. In this paper, we introduce a textual case-based reasoning (TCBR) framework for KM systems that manipulates organizational knowledge embedded in artifacts (e.g., best practices, alerts, lessons learned). The TCBR approach acquires knowledge from human users (via knowledge elicitation) and from text documents (via knowledge extraction) usi ng template-based information extraction methods, a subset of natural language, and a domain ontology. Organizational knowledge is stored in a case base and is distributed in the context of targeted processes (i.e., within external distribution systems). The knowledge artifacts in the case base have to be translated into the format of the external distribution sy...

Proceedings of the …, 2000

Exploiting lessons learned is a key knowledge management (KM) task. Currently, most lessons learned systems are passive, stand-alone systems. In contrast, practical KM solutions should be active, interjecting relevant information during decision-making. We introduce an architecture for active lessons delivery systems, an instantiation of it that serves as a monitor, and illustrate it in the context of the conversational case-based plan authoring system HICAP (Muñoz-Avila et al., 1999). When users interact with HICAP, updating its domain objects, this monitor accesses a repository of lessons learned and alerts the user to the ramifications of the most relevant past experiences. We demonstrate this in the context of planning noncombatant evacuation operations.

Proc. of FLAIRS

Lessons learned systems (LLS) are systems that support a lessons learned process (LLP) to collect, verify, store, disseminate, and reuse organizational lessons. In this pa-per we examine technological, human, and managerial problems that contribute to the limited ...

… in Knowledge Engineering, R. Jain, A. …, 2003

Knowledge-based knowledge management (KBKM) focuses on applications of knowledge-based systems (KBS) tailored to knowledge management (KM) problems. Although KM is primarily concerned with how people and organizations utilize their knowledge assets, one key to doing so efficiently is to employ technology to facilitate the KM process. One particular kind of technology has shown itself to be extremely useful in this context -specifically the technology of knowledge-based systems. In this chapter we discuss the relationship of KM to knowledge-based technology and provide an exposition of three fundamental knowledge-based methodologies that can facilitate knowledge managementexpert systems, case-based reasoning and ontologies.