Knowledge-based systems from a socio-cognitive 1 perspective

. Recent trends in the design and development of knowledge-based systems KBSs are discussed with special emphasis on issues that relate to situated knowledge. A knowledge base is regarded as a model of expertise that acknowledges the embeddedness of expert knowledge in social interaction and in the workplace in general. KBS development is viewed as an instance of socio-technical design. Experience from several European projects is recounted to illustrate the issues addressed. Suggestions for KBS development are presented as methodological guidelines, with special emphasis on systems employing case-based reasoning.     

Knowledge-based systems and issues of integration: A commercial perspective

Commercial applications of knowledge-based systems are changing from an embryonic to a growth business. Knowledge is classified by levels and types to differentiate various knowledge-based systems. Applications are categorized by size, generic types, and degree of intelligence to establish a framework for discussion of progress and implications. A few significant commercial applications are identified and perspectives and implications of these and other systems are discussed. Perspectives relate to development paths, delivery modes, types of integration, and resource requirements. Discussion includes organizational opportunities and impacts, business perspectives, workplace issues, public effects, and educational needs. Common traps and modes of failures are mentioned.Major conclusions point to the great value of knowledge-based systems when development is pursued correctly, and to the existence of insufficient understanding of resource requirements, needs for integration, different talents, and incorporation into operational strategy to equitably exploit the significant economic and social costs associated with their creation and use.     

Knowledge-based man-machine systems

A new method of increase user gain in man-machine interaction is discussed. In particular, attention is directed to describing a model in the computer. An intelligent system provided with the knowledge base and the inference mechanism is proposed for the purpose, whereby illustration is given of the system describing both structure and attributes of the real object in the same framework.     

Adoption of open source software in organizations: A socio-cognitive perspective

Open source software (OSS) is an important trend in the information technology adoption landscape. It has received considerable attention in the scientific literature, but mostly in the professional press. In fact, there is much debate over its actual commercial and organizational value. Since the public discourse accompanying an IT may influence adoption decisions, it is important to consider IT specialists’ perceptions of the discourse on OSS. In this study, we investigated the relationship between IT specialists’ profiles, IT specialists’ reception of the public discourse on OSS, and their organizations’ receptivity to OSS. Drawing on the socio-cognitive perspective of IT innovation adoption and the organizing vision theory, a survey of 271 IT specialists was conducted to examine these issues. Our results indicate that a majority of IT specialists in our sample are rather neutral about the OSS concept conveyed in the public discourse. However, our sample also comprises respondents with more extreme perceptions who can be classified as either supporters or detractors. Our results indicate that detractors have more years of experience but have been less exposed to OSS than supporters, and that IT specialists’ perceptions of the OSS concept are positively associated with their organizations’ openness to OSS adoption and, to a lesser extent, with the existence of an organizational policy that favors OSS adoption. Altogether, our findings provide strong support for the organizing vision theory and the idea that the popularity of an IT innovation concept favors the adoption of the material IT innovation in organizations. By providing a preliminary test of a nomological network of IT specialists’ perceptions of the OSS concept, our study offers insights as to why organizations may or may not take OSS into account in their software procurement decisions.     

The web from a complex adaptive systems perspective

The web continues to grow at a phenomenal rate, and the amount of information on the web is overwhelming. Finding the relevant information remains a big challenge. Due to its wide distribution, its openness and high dynamics, the web is a complex system, for which we have to imagine mechanisms of content maintaining, filtering and organizing that are able to deal with its evolving dynamics and distribution. Integrating mechanisms of self-organization of the web content is an attractive perspective, to match with these requirements. Self-organized complex systems can be programmed using situated multi-agent systems with a coupling between the agents' social organization and spatial organization. This paper explores the web from a complex adaptive system (CAS) perspective. It reviews some characteristic behaviors of CASs and shows how the web exhibits similar behaviors. We propose a model and a prototype of a system that addresses the dynamic web content organization, adopting the CAS vision and using the multi-agent paradigm.     

Learning classifier systems from a reinforcement learning perspective

 We analyze learning classifier systems in the light of tabular reinforcement learning. We note that although genetic algorithms are the most distinctive feature of learning classifier systems, it is not clear whether genetic algorithms are important to learning classifiers systems. In fact, there are models which are strongly based on evolutionary computation (e.g., Wilson's XCS) and others which do not exploit evolutionary computation at all (e.g., Stolzmann's ACS). To find some clarifications, we try to develop learning classifier systems “from scratch”, i.e., starting from one of the most known reinforcement learning technique, Q-learning. We first consider thebasics of reinforcement learning: a problem modeled as a Markov decision process and tabular Q-learning. We introduce a formal framework to define a general purpose rule-based representation which we use to implement tabular Q-learning. We formally define generalization within rules and discuss the possible approaches to extend our rule-based Q-learning with generalization capabilities. We suggest that genetic algorithms are probably the most general approach for adding generalization although they might be not the only solution.     

Knowledge-based construction of distributed constrained systems

The problem of deriving distributed implementations from global specifications has been extensively studied for a number of application domains. We explore it here from the knowledge perspective: A process may decide to take a local action when it has enough knowledge to do so. Such knowledge may be acquired by communication through primitives available on the platform or by static analysis. In this paper, we want to combine control and distribution, that is, we need to impose some global control constraint on a system executed in a distributed fashion. To reach that goal, we compare two approaches: either build a centralized controlled system, distribute its controller and then implement this controlled system on a distributed platform; or alternatively, directly enforce the control constraint while implementing the distributed system on the platform. We show how to achieve a solution following the second approach and explain why this is a pragmatic and more efficient strategy than the other, previously proposed one.     

Knowledge-based systems in computer-assisted production — a review

With the development of ‘expert’ or ‘knowledgeåsed’ systems, the efforts of artificial intelligence research have attained commercial application. While there have been to date, only a few investigations concerning the extent of this transfer, assessment of the extent of manufacturing applications has been mostly the result of speculation. By means of a comprehensive survey of documented systems in the USA, UK and FRG, the state-of-the-art of production applications of knowledge-based systems is described. Detailed discussion i is made of reasons leading to computer assistance and integrated solutions. Computer-assisted technological s systems are reviewed under their specific task areas — that is, construction and design, production planning, production control, production of parts, quality assurance, assembly, maintenance and customer service. Solutions for the connection of islands of automation are dealt with under the heading of Computer Integrated Manufacturing.     

Knowledge-based systems and fuzzy boolean programming

This article discusses the applications of fuzzy boolean programming problems for representing and reasoning with propositional knowledge. the use of the models provided by the fuzzy boolean problems are proposed to answer imprecise questions in precisely stated knowledge-based systems. Also, the advantages of using fuzzy boolean programming instead of classical ones are presented in the framework of propositional knowledge. © 1994 John Wiley & Sons, Inc.     

Distributed artificial intelligence from a socio-cognitive standpoint: Looking at reasons for interaction

Distributed Artificial Intelligence (DAI) deals with computational systems where several intelligent components interact in a common environment. This paper is aimed at pointing out and fostering the exchange between DAI and cognitive and social science in order to deal with the issues of interaction, and in particular with the reasons and possible strategies for social behaviour in multi-agent interaction is also described which is motivated by requirements of cognitive plausibility and grounded the notions of power, dependence and help. Connections with human-computer interaction are also suggested.     

Evaluation of EIS from a managerial perspective1

Abstract. While executive information systems (EIS) constitute a rising trend in the use of information technology in organizations, much is still unknown in terms of the attributes an EIS should possess in order to increase the probability of successful implementation. A survey was conducted on 28 Canadian organizations, with 52 users giving their perceptions on the various attributes of EIS. Results indicate that the more important aspects relate to the quality of information and the benefits provided by EIS. Less important are the user-interface and technical capabilities. User satisfaction with the attributes of their present system was also measured. In this regard, managers were more content with the quality of information and the user-interface than with the benefits and the technical capabilities of their EIS. Overall, top-managers were less satisfied with the present system than lower-level managers.     

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.     

Opportunities for computer crime: considering systems risk from a criminological perspective

Systems risk refers to the likelihood that an Information System (IS) is inadequately protected against certain types of damage or loss. While risks are posed by acts of God, hackers and viruses, consideration should also be given to the ‘insider’ threat of dishonest employees, intent on undertaking some form of computer crime. Against this backdrop, a number of researchers have addressed the extent to which security managers are cognizant of the very nature of systems risk. In particular, they note how security practitioners' knowledge of local threats, which form part of such risk, is often fragmented. This shortcoming contributes to situations where risk reducing efforts are often less than effective. Security efforts are further complicated given that the task of managing systems risk requires input from a number of departments including, for example, HR, compliance, IS/IT and physical security. To complement existing research, and also to offer a fresh perspective, this paper addresses systems risk from the offender's perspective. If systems risk entails the likelihood that an IS is inadequately protected, this text considers those conditions, within the organisational context, which offer a criminal opportunity for the offender. To achieve this goal a model known as the ‘Crime-Specific Opportunity Structure’ is advanced. Focusing on the opportunities for computer crime, the model addresses the nature of such opportunities with regards to the organisational context and the threats posed by rogue employees. Drawing on a number of criminological theories, it is believed the model may help inform managers about local threats and, by so doing, enhance safeguard implementation.     

Towards understanding socio-cognitive behaviors of crowds from visual surveillance data

Multimedia Tools and Applications - The problem of understanding socio-cognitive aspects of crowd behavior is a challenging yet critical task particularly for human-computer interaction...     

KB-SCHED: Knowledge-based scheduler for complex and dynamic systems

Scheduling problems are becoming more and more complex everyday. This makes the current rules and algorithms difficult to comply with the requirements. New machines with the capabilities of processing more than one jobs is being developed. Sometimes one job is divided into parts and processed by more than one machine at the same time. These make the current algorithms insufficient. Artificial intelligence technologies, especially expert systems are proven to deal with such dynamic complex problems in several domains. In this study, an example of such a complex problem is introduced and knowledge-based scheduling for these kind of problems is elaborated with a real life industrial example.     

Knowledge-based instrument systems Integrating knowledge software with microprocessor-based instruments

Integrating knowledge software with existing microprocessor-based instruments leads to a new generation of instrument systems. Our basic idea is to build an instrument system supported by a microcomputer workstation running knowledge software. The initial phase of implementation has been realised on a microprocessor-controlled automatic blood gas analyzer, interfaced to a Great-Wall 0520C-H, an IBM-XT upwardly-compatible workstation. The implementation of a knowledge system module which is programmed in LISP with lists expressed in Chinese characters and transformed into C which provides more compact code is described. It obtains a virtual link between instrument nodes according to the author's model. Further progress leads to a bedside instrument system of a life-support system for paediatric cardiac surgical patients. It serves as a testbed for exploring principles and guidelines for designing and implementing knowledge-based instrument systems as defined in the present paper.     

Knowledge-based technology for solving complex systems of interrelated problems

The solution of a system of problems is modeled as a search among alternative paths on a semantic network. We consider representation of procedural knowledge and modeling of decision processes by production rules and Petri nets.Translated from Kibernetika, No. 3, pp. 108–111, May–June, 1991.     

Knowledge-based systems in medicine--a Nordic research and development programme

A Nordic research and development programme, 'KBS in Medicine' (KUSIN-MEDICINE), was run in 1986-89. Its main goal was to acquire an understanding of applying knowledge-based techniques in medicine and of the limitations of present-day artificial intelligence (AI) methodologies. The programme comprised four experimental installation sites (Tampere in Finland, Uppsala and Link?ping in Sweden, and Aalborg in Denmark) each prototyping in one or more medical domains. The programme was financed by the Nordic Fund for Technological and Industrial Development, by national funds for applied research and by a number of industries. Prototype decision support systems were produced in the following domains: intensive care (Tampere, Uppsala, Link?ping, Aalborg), clinical chemistry (Tampere, Uppsala) and clinical neurophysiology (Aalborg in collaboration with Turku and Uppsala). These served to transfer this technology to industry and helped to identify limitations of this technology.     

Knowledge-based systems in civil engineering: Three case studies

Although substantial advances have been achieved in last twenty years, in practice we are still far from taking full advantage of the potential of knowledge-based systems (KBS). Very few KBS have survived their evaluation period and only one third of those that were initially reported as successful are still in use. The primary purpose of the paper is to report on three KBS applications that have been developed for research purposes in the field of civil engineering. The main challenge underlying all three projects has been to develop computer support systems that would induce changes and improvements to the way that engineers solve their everyday problems. A great deal of effort has been put into eliciting knowledge and reasoning strategies from engineering experts with the aim of building up a computer model of their expertise in order to assist engineers in their decision-making processes. The paper closes by highlighting the principal achievements and the main issues concerning the future development of KBS for solving real life problems.