Finite-Base Revision Supporting Knowledge Management and Decision Making

Finite-Base Revision Supporting Knowledge Management and Decision Making

Fátima C.C. Dargam (SimTech Simulation Technology – Graz, Austria and ILTC, Instituto Doris Aragon – Rio de Janeiro, Brazil)
DOI: 10.4018/978-1-59904-843-7.ch043
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Abstract

Generation and most of all sustainability of organizational success rely heavily on proper decision making and on the application of knowledge management (KM) concepts, where knowledge-based structures are fundamental components. KM can also be viewed as a means to support enhanced decision making through effective control of organizational knowledge. One of the main goals of KM is to capture, codify, organize, and store relevant knowledge into repositories, knowledge bases (KB), for later retrieval and use by organizations. However, there is always the danger of accumulating knowledge in an increasingly vast way, such that it becomes impossible to process it when necessary. Therefore, appropriate technologies have to be identified to protect us from irrelevant information. As the study in Handzic (2004) shows, decision-makers need to pursue primarily one KM strategy in order to use knowledge effectively. Moreover, the codification KM strategy using procedural knowledge maps was proven to be quite appropriate for solving decision problems of a complex nature. It is commonly agreed that KM can bridge the existing information and communication gaps within organizations, consequently improving decision making (Dargam & Rollett, 2007).

Key Terms in this Chapter

Contraction: Belief change that happens when the agent is forced to retract some beliefs.

Finite Base Revision: When the focus of the revision approach is done on a finite set of beliefs, it is then usually called Base Revision or Finite-Base Revision.

Equivocality: In the context of this work, the state of having several competing or contradictory conceptual frameworks.

Ambiguity: In the context of this work, the fact of not having a conceptual framework for interpreting information.

Revision: Belief change that deals with the acceptance of new information contradicting the current belief set; therefore, a subsequent process of restoring the consistency of that belief set is needed, whenever the new information is not itself contradictory. The notion of revision was introduced with the AGM belief revision theory (Alchourrón & Makinson, 1982, 1985, 1986; Alchourrón et al., 1985).

Compromise Revision: A finite base revision approach originally proposed in Dargam (1996). The compromises adopted are of two types: (1) If the input sentence cannot be added to the knowledge base K, we compromise by allowing its consistent consequences to be added to K . This case is referred to as “the input compromise.” (2) The input can be added to the base, K is revised and we compromise by allowing the consistent consequences of the retracted sentences to be in the revised base. This case is referred to as “the retracted sentences compromise.” By adopting these compromises, less loss of information from the requested update of the knowledge base is achieved.

Safe-Maximal Set: A set that is obtained either in the input compromise case and or in the retracted sentences compromise case, by using the safe-maximality notion. It is not necessarily inclusion-maximal.

Belief Revision: This approach reflects the kind of information change in which an agent reasoning about his beliefs about the world is forced to adjust them in face of a new, and possibly contradictory, piece of information.

Belief Set: When the set of beliefs held by an agent is closed under the consequence relation of some formal language, it is usually called a belief set.

Complexity: In the context of this work, the case of having more information than one can easily process.

Expansion: Belief change that occurs when new information is consistent with the belief set.

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