Leonardo Mancilla-Amaya

Decisional DNA: A multi-technology shareable knowledge structure for decisional experience

Knowledge representation and engineering techniques are becoming useful and popular components of hybrid integrated systems used to solve complicated practical problems in different disciplines. These techniques offer features such as: learning from experience, handling noisy and incomplete data, helping with decision making, and predicting capabilities. In this paper, we present a multi-domain knowledge representation structure called Decisional DNA that can be implemented and shared for the exploitation of embedded knowledge in multiple technologies. Decisional DNA, as a knowledge representation structure, offers great possibilities on gathering explicit knowledge of formal decision events as well as a tool for decision making processes. Its applicability is shown in this paper when applied to different decisional technologies. The main advantages of using the Decisional DNA rely on: (i) versatility and dynamicity of the knowledge structure, (ii) storage of day-to-day explicit experience in a single structure, (iii) transportability and shareability of the knowledge, and (iv) predicting capabilities based on the collected experience. Thus, after analysis and results, we conclude that the Decisional DNA, as a unique multi-domain structure, can be applied and shared among multiple technologies while enhancing them with predicting capabilities and facilitating knowledge engineering processes inside decision making systems.

Application of a Multi-domain Knowledge Structure: The Decisional DNA

Knowledge engineering techniques are becoming useful and popular components of hybrid integrated systems used to solve complicated practical problems in different disciplines. Knowledge engineering techniques offer features such as: learning from experience, handling noisy and incomplete data, helping with decision making, and predicting. This chapter presents the application of a knowledge structure to different fields of study by constructing Decisional DNA. Decisional DNA, as a knowledge representation structure, offers great possibilities on gathering explicit knowledge of formal decision events as well as a tool for decision making processes. Its versatility is shown in this chapter when applied to decisional domains in finances and energy. The main advantages of using the Decisional DNA rely on: (i) versatility and dynamicity of the knowledge structure, (ii) storage of day-to-day explicit experience in a single structure, (iii) transportability and share ability of the knowledge, and (iv) predicting capabilities based on the collected experience. Thus, after showing the results, we conclude that the Decisional DNA, as a unique structure, can be applied to multi-domain systems while enhancing predicting capabilities and facilitating knowledge engineering processes inside decision making systems.

USING SET OF EXPERIENCE KNOWLEDGE STRUCTURE TO EXTEND A RULE SET OF CLINICAL DECISION SUPPORT SYSTEM FOR ALZHEIMER'S DISEASE DIAGNOSIS

In this article we present an experience-based clinical decision support system (CDSS) for the diagnosis of Alzheimers disease, which enables the discovery of new knowledge in the system and the generation of new rules that drive reasoning. In order to evolve an initial set of production rules given by medical experts we make use of the Set of Experience Knowledge Structure (SOEKS). An illustrative case of our system is also presented.

DECISIONAL DNA: THE CONCEPT AND ITS IMPLEMENTATION PLATFORMS

Knowledge and experience engineering techniques are becoming increasingly useful and popular components of hybrid integrated systems used to solve complex real-life problems in different disciplines. These techniques offer features such as learning from experience, handling noisy and incomplete data, helping with decision making, and predicting capabilities. In this article, we present a number of different applications of a multidomain knowledge representation structure called decisional DNA that can be implemented and shared for the exploitation of embedded knowledge within different technologies.

SMART KNOWLEDGE-SHARING PLATFORM FOR E-DECISIONAL COMMUNITY

Knowledge plays a major role in enterprises, given its importance as a significant organizational asset. In order to solve problems and support complex decision-making processes, knowledge and experience have to be transmitted between diverse individuals and organizations. Thus, knowledge-sharing can be considered a fundamental element in any knowledge-oriented process, because it fosters collaboration, and facilitates experiential knowledge discovery, distribution, and use. We present the E-Decisional Community, a proposal for an integrated knowledge-sharing platform where several entities are able to share experiential knowledge. Its main concern is to promote experiential knowledge evolution and sharing through generations of decision makers, aiming at the creation of a marketplace where knowledge is provided as a service.

USING HUMAN BEHAVIOR TO DEVELOP KNOWLEDGE-BASED VIRTUAL ORGANIZATIONS

Virtual organizations promote dynamic interaction between individuals, groups, and organizations, who share their capabilities and resources to pursue a common goal and maximize their benefits. Among these resources, knowledge is a critical one that requires special attention in order to support problem-solving activities and decision-making processes and provide strategic advantage. This article presents an initial proposal for the creation of dynamic knowledge-based virtual organizations, as a way to share knowledge in order to support problem-solving activities. This approach is based on behavioral elements, identified by other researchers, that affect group interactions; these items are represented inside the e-decisional community in a manner that allows software agents to interact similarly to their human counterparts. An initial model and a functional prototype have been developed and used to obtain a set of preliminary results, which show human-like behavior in our test system.

QUALITY ASSESSMENT OF EXPERIENTIAL KNOWLEDGE

In recent years knowledge has been considered a critical organizational asset. As any other asset, knowledge provides value to organizations only when it conforms to a set of specifications and standards; in other words, when it is of good quality. Several proposals have addressed the issue of quality of knowledge, but there is not a widely accepted way of measuring such a concept. This article introduces a new approach to measure explicit knowledge in a semi-automatic way using software agents. The ideas described in this article are part of the e-Decisional Community concept, an agent-based platform for sharing experiential knowledge. The results of this research process show that it is possible to obtain a percentage of knowledge that represents an approximate measure of an individuals knowledge.

Knowledge-based virtual organizations for the E-decisional community

Virtual Organizations (VOs) promote dynamic interaction between individuals, groups and organizations, who share their capabilities and resources to pursue a common goal and maximize their benefits. Among these resources, knowledge is a critical one that requires special attention in order to support problem-solving activities, decision making processes and provide strategic advantage. In this paper, we present an initial proposal for the creation of dynamic knowledge-based VOs inside the E-Decisional Community, an integrated knowledge sharing platform that aims at the creation of markets where knowledge is provided as a service. This approach will provide technological support for discovering, re-using evolving and sharing experiential knowledge represented by means of the Set of Experience Knowledge Structure (SOEKS) and Decisional DNA among several entities.

ESTIMATING KNOWLEDGE QUANTITY IN THE E-DECISIONAL COMMUNITY

Organizations plan their projects and activities based on the availability of their assets, which often range from manufactured elements to computer services and infrastructure. In todays economy, a new asset comes into play: knowledge. Knowledge has become the most valuable resource for many organizations, and its proper use often determines the survival of enterprises in a competitive environment. However, determining how much knowledge is accessible is not as simple as counting how many units of a product are in inventory. Measuring knowledge quantity has been the focus of active research recently. This article presents an approach for knowledge quantification that offers a novel way of estimating the depth of an agents knowledge in an automated way. The knowledge quantity measures described in this article are used in the e-decisional community, an integrated knowledge sharing platform that aims at the creation of markets where knowledge is provided as a service.

A PROPOSAL FOR A KNOWLEDGE MARKET BASED ON QUANTITY AND QUALITY OF KNOWLEDGE

Autonomous market environments have been proposed in the literature as the future of electronic markets. The ability to delegate complex negotiation processes and obtain similar or better results than their human counterparts has generated a great interest in agent-based markets. More recently, such a paradigm has been applied in the field of knowledge management and, more specifically, to knowledge sharing and exchange; however, most of the knowledge market proposals in the literature fail to give details on a key component of their models: knowledge quality. This article presents a new proposal for an agent-based market environment that aims at filling the previously mentioned gap in research. The main contribution of our research is the integration of formal mechanisms for knowledge quality and quantity measurement and the use of these values to set a price for knowledge and select the most suitable agent for negotiation.