Intelligent Decision Technologies

A person adds new knowledge to his/her mind, taking into account new information, additional details, better precision, synonyms, homonyms, redundancies, apparent contradictions, and inconsistencies between what he/she knows and new knowledge that he/she acquires. This way, he/she incrementally acquires information keeping it at all times consistent. This information can be represented by Ontologies. In contrast to human approach, algorithms of Ontologies fusion lack these features, merely being computer-aided editors where a person solves the details and inconsistencies. This article presents a method for Ontology Merging (OM), its algorithm and implementation to fuse or join two ontologies (obtained from Web documents) in an automatic fashion (without human intervention), producing a third ontology, and taking into account the inconsistencies, contradictions, and redundancies between both ontologies, thus delivering a result close to reality. The repeated use of OM allows acquisition of much information about the same topic.

This paper examines the driving and opposing forces that are governing the current paradigm shift from a data-processing information technology environment without software intelligence to an information-centric environment in which data changes are automatically interpreted within the context of the application domain. The driving forces are related to the large quantity of data and the complexity of networked systems that both call for software intelligence. The opposing forces are non-technical and due to the natural human resistance to change.

Based on this background the paper describes current information-centric technology, proposes a vision of intelligent software system capabilities, and identifies four areas of necessary research. Most urgent among these are the ability to dynamically extend and merge ontologies and semantic search capabilities that can be initiated either by human users or software agents. Longer term research interests that pose a more severe challenge are related to the translation of emerging theoretical hierarchical temporal memory (HTM) concepts into usable software capabilities and the automated interpretation of graphical images such as those recorded by surveillance video cameras.

Much of knowledge management has been aimed at capturing, converting and connecting information and knowledge as it is generated in an organization. As a result, knowledge management is focused on the past and present, providing decision makers information and knowledge. Decision makers are then responsible for using that knowledge to anticipate the future. As a result, knowledge management systems generally do not have a capability to anticipate the future. Thus, there is interest in understanding how knowledge management systems will be able to accommodate anticipation of the future at the systems level.

One approach is the use of so-called "mirror worlds." The concept of a mirror world is based on a bold assertion: "You will look into a computer screen and see reality." With mirror worlds, managers could be proactive, anticipating what might happen and acting accordingly, instead of waiting till events happen and then reacting. Using transaction and other data, information and knowledge, mirror worlds of companies could be built in order to anticipate the future. This paper compares mirror worlds to other virtual worlds, evaluates the state of mirror worlds and examines potential limitations of such constructs for predictive knowledge management.

This paper explores novel approaches under the design inquiry paradigm that promise to help organizations better understand and solve socio-technical dilemmas. Design inquiry is contrasted with scientific inquiry (Section 1). Section 2 presents a meso-scale model of models methodology for design inquiry that synthesizes systems science, agent modeling and simulation, knowledge management architectures, and domain theories and knowledge. The goal is to focus computational science on exploring underlying mechanisms (white box modeling) and to support reflective theorizing and discourse to explain social dilemmas and potential resolutions. Section 3 then describes an evolving agent modeling and simulation testbed while Section 4 offers two gameworld applications that implement this approach and that serve as an example of the new types of instruments useful for systems social science. The conclusions wrapup by reviewing lessons learned about 10 criteria that have guided this research.

Standards and models of processes – such as ISO/IEC 9000 standard for deploying quality management systems – have been developed for international organizations to promote the utilization of best managerial and engineering practices. However, given their conceptual density, a large number of concepts, composite concepts, and interrelationships, understanding them cannot be considered a trivial cognitive task for new readers and decision makers. Consequently, some IT-supported systems have been used to improve such an understanding. These tools provide basic searching services via keywords or catalog indices on monolithic text-based, hypertext technology, or multimedia documents. As a result, their system performance, correctness, user satisfaction, and lately understanding metrics, are still unsatisfactory. In this paper, we support the hypothesis that IT systems, where knowledge structures are explicitly codified, such as ontology-based knowledge management systems (o-KMS), can provide a plausible better solution. To support this hypothesis, we firstly report a conceptual survey on KMS and KM foundations (IT for KMS, KM Processes) to describe the capabilities of KMS and supporting needs for KM processes. Secondly, we describe the profiles of standards and models of process, and review related research on KMS. Finally, we present the problem-solution links, and discuss how o-KMS might provide a better support. We conclude that research results to date require further advance.