Organizational Semiotics: A Normative Agent-Based Approach to VE Modelling

This article presents some basic aspects of the proof theory of first order logic programming. We start off from general resolution, unit resolution, input resolution and SLD resolution. Then we turn to the sequent calculus and its relationship to resolution and present deductive systems for logic programs (with negation). After discussing partiality in logic programming, this article ends with the introduction of inductive extensions of logic programs, which provide a powerful proof-theoretic framework for logic programming.

A virtual enterprise has been considered as a temporary consortium of companies. Recently, researchers have investigated virtual enterprises from various perspectives, such as business activities and workflows, information exchanges and their standards, organizational behaviours between companies, etc. However, real collaborative activities among companies in a virtual enterprise cannot be done before a partnership is developed among companies. Besides, though partnership is important in virtual enterprises, a decision model for partnership development is seldom studied. This research develops a decision model from a micro view (i.e. from a company's perspective). A company may use the model to specify its current partnership strategy and determine its future partnership strategy with another company. Four factors are considered in the model: the format of the relationship with suppliers, the number of suppliers, the type of service provided by suppliers and the method of service delivery from suppliers. However, a partnership strategy has to be updated based on internal and external changes. This research finds out that six factors are associated with a change on partnership. With those factors, a decision process is developed for a company to update its partnership strategy. The partnership of IBM to Acer is also investigated to illustrate the application of the model. The results validate the applicability of the decision model.

The growth of mobile technologies and smartphones is reshaping the individual and organisational behaviour which affect the business environment. One of the key challenges of mobile payment is how to understand and manage user expectations and technology acceptance. Therefore, to better understand mobile payment use and acceptance, we need to analyse the factors and barriers that influence technology use. The investigation uses Technology Acceptance Model in conjunction with Organisational Semiotics, a socio-technical method of design, to overcome possible limitations addressed in research. This approach offers methods that can help to develop a research model for mobile payment use focusing on technical and social aspects.

Debugging is a process of finding and reducing the number of bugs, or defects, in a program. A defect typically is detected because the program generates unexpected behaviour. In order to locate the cause of a defect, it therefore is essential to explain why this behaviour is generated. In this paper, we propose a new debugging approach for agent-oriented programming that exploits the structure and basic concepts that are used in logic-based agent programs for providing answers to some of such why questions. Our approach is based on the fact that the behaviour of an agent-oriented program can be traced back to basically two sources. First, agents derive their choice of action from their beliefs and goals - two fundamental programming concepts in agent-oriented programming. These folk psychological concepts provide reasons for doing something. Second, agent programs are rule-based programs and the evaluation of rules to a large extent determines the program’s behaviour.

摘要: 通过对16家制造型企业中的343个员工、662个同事及343个直接领导的配对数据进行实证分析,文章探讨了员工的组织政治技能、政治知觉,以及他们与直接上司的关系对于员工职业发展的关系。多层线性分析模型(HLM)结果表明:员工的政治技能有助于促进其与领导之间形成良好的关系(guanxi),并籍此积极影响个人在组织中的职业发展,员工-领导关系是政治技能与职业发展之间的中介变量。另外,组织政治知觉影响政治技能对领导关系的作用,在政治氛围浓重的组织中,员工-领导关系更易受到员工政治技能的影响

Locus of control is an important variable for the explanation of human behavior in organizations . The nature of the concept, its measurement, and general evidence for its validity are discussed. Several hypotheses are presented involving the locus of control in an organizational context, and supporting evidence from applied studies is reviewed. Specifically, it is suggested that locus of control is related to motivation, effort, performance, satisfaction, perception of the job, compliance with authority, and supervisory style. Furthermore, locus of control may moderate the relation between incentives and motivation and between satisfaction and turnover. Little attention has been given to individual personality in research on job motivation and satisfaction. For the most part, the major theories in organizational psychology assume that the same basic processes account for behavior across all individuals and that situational characteristics cause predictable behavior across people.1 This article attempts to demonstrate the usefulness of personality in explaining human behavior in organizations and focuses on locus of control as it relates to behavior in organizational settings. The general theory of locus of control arose from observation and research in clinical psychology. Both the measurement and theory have been refined so that the concept is heuristically useful. Over two dozen studies of locus of control have been related specifically to attitudinal, motivational, and behavioral variables in organizational settings. One major task of this article is to integrate the general theory with the organizational findings.

We describe a relational learning by observation framework that automatically creates cognitive agent programs that model expert task performance in complex dynamic domains. Our framework uses observed behavior and goal annotations of an expert as the primary input, interprets them in the context of background knowledge, and returns an agent program that behaves similar to the expert. We map the problem of creating an agent program on to multiple learning problems that can be represented in a “supervised concept learning’’ setting. The acquired procedural knowledge is partitioned into a hierarchy of goals and represented with first order rules. Using an inductive logic programming (ILP) learning component allows our framework to naturally combine structured behavior observations, parametric and hierarchical goal annotations, and complex background knowledge. To deal with the large domains we consider, we have developed an efficient mechanism for storing and retrieving structured behavior data. We have tested our approach using artificially created examples and behavior observation traces generated by AI agents. We evaluate the learned rules by comparing them to hand-coded rules.

The tutorial is intended to introduce logic programmers to advanced Internet programming. Our tools, encapsulated in a small set of very high-level primitives, will enable the participants to program basic client-server applications in a few lines of code, interact with http servers and Java applets, and put on the Web their favorite programs. The tutorial also gives a glimpse on mobile code and multi-agent programming with special emphasis on using essential Prolog features like meta-programming to implement complex remote execution mechanisms, while language extensions like hypothetical assumptions and Linda coordination primitives are used to elegantly deal with the complexities of network and agent programming, at a very high level. Mobile threads are implemented by capturing first order continuations in a compact data structure sent over the network. Code is fetched lazily from its original base turned into a server as the continuation executes at the remote site. Combined with a dynamic recompilation scheme ensuring that heavily used code moves up smoothly in a speed hierarchy, our techniques are shown to be an effective means for implementing mobile agents in a natural programming style. We will also describe our experience with building a comprehensive Internet programming infrastructure [3] on top of BinProlog [2] and its Java peer - a multithreaded unification based Linda client and server, using Prolog terms and unification for associative search. Among the applications, we review LogiMOO[5, 4], a shared multi-user virtual world prototype with a controlled natural language interface, executing under Java and VRML enabled browsers like Netscape Communicator. The tutorial is organized around a set of “how to do it” examples, among which the following: • simple CGI-scripts, client-server applications on the WWW • a multi-user chat program • an applet: from Prolog to Java and back • distributed programming with BinProlog engines and threads • mobile code: “take the continuation and run” • LogiMOO: crafting shared virtual objects on the Web • Talking to VRML: BinProlog-driven 3D-avatar animation We conclude with an analysis of the practical application development and promising future directions of work. With the paradigm shift to highly interconnected computers and programming tools, logic programming languages have a unique opportunity to contribute to practical Internet application development. Simplicity, robustness, automatic memory management, code and object mobility [1] are among the features some LP languages [6, 3] share with emerging tools like Java. Superior meta-programming and high-level networking, built-in grammars and dynamic databases, declarative semantics are among their competitive advantages. In this context, we discuss some strategies about how to integrate this technology in the (real!) world of software development.

Prior research highlighted the importance of an organisational context stimulating autonomous behaviour when trying to increase levels of corporate entrepreneurship. From a social exchange perspective, we argue that firms in developing countries need to complement such supportive practices with performance-oriented practices. Our findings indicate that Iranian firms with an organisational context characterised by an interaction of social context and performance management have more engagement in corporate entrepreneurship, and that corporate entrepreneurship mediated the relationship between the organisational context and firm performance. This provides a better understanding of the way firms in developing economies can shape their organisational context to promote corporate entrepreneurship in order to achieve better firm performance.

Normative modelling is becoming more popular in neuroimaging due to its ability to make predictions of deviation from a normal trajectory at the level of individual participants. It allows the user to model the distribution of several neuroimaging modalities, giving an estimation for the mean and centiles of variation. With the increase in the availability of big data in neuroimaging, there is a need to scale normative modelling to big data sets. However, the scaling of normative models has come with several challenges.So far, most normative modelling approaches used Gaussian process regression, and although suitable for smaller datasets (up to a few thousand participants) it does not scale well to the large cohorts currently available and being acquired. Furthermore, most neuroimaging modelling methods that are available assume the predictive distribution to be Gaussian in shape. However, deviations from Gaussianity can be frequently found, which may lead to incorrect inferences, particularly in the outer centiles of the distribution. In normative modelling, we use the centiles to give an estimation of the deviation of a particular participant from the ‘normal’ trend. Therefore, especially in normative modelling, the correct estimation of the outer centiles is of utmost importance, which is also where data are sparsest.Here, we present a novel framework based on Bayesian linear regression with likelihood warping that allows us to address these problems, that is, to correctly model non-Gaussian predictive distributions and scale normative modelling elegantly to big data cohorts. In addition, this method provides likelihood-based statistics, which are useful for model selection.To evaluate this framework, we use a range of neuroimaging-derived measures from the UK Biobank study, including image-derived phenotypes (IDPs) and whole-brain voxel-wise measures derived from diffusion tensor imaging. We show good computational scaling and improved accuracy of the warped BLR for certain IDPs and voxels if there was a deviation from normality of these parameters in their residuals.The present results indicate the advantage of a warped BLR in terms of; computational scalability and the flexibility to incorporate non-linearity and non-Gaussianity of the data, giving a wider range of neuroimaging datasets that can be correctly modelled.

Emotions act as an interface between environmental stimuli and behavioral output. Emotional states directly influence performance-relevant behaviors and cognitive processes, which in turn influence performance. An individual in an organization exhibits effective behavior if the individual is in right emotional state. The process of emotion regulation shows the way in which emotions influence performance in organizations. Emotions are defined as discrete, functional, bio-social action and expression systems in human beings (Fischer, Shaver, & Camochan, 1990). Emotions result in a sequence of events involving core affect, appropriate behavior with respect to the emotional stimuli, and emotional experience (Russell & Barrett, 1999). In the organizational context, Ashkanasy, Hartel, and Zerbe (2000) have defined emotions in terms of endogenous and exogenous inputs to neural systems, leading to internal and external manifestations of emotion. The internal manifestations of emotion include the subjective feelings experienced by the individual when subjected to emotion-eliciting stimulus, which the individual cognitively interprets. The external manifestations of emotion include facial expressions and posture. Importantly, it is the external manifestations of emotion that are subject to interpretation both by the individual affected and by others in the environment. As per Affective Events Theory (Weiss & Cropanzano, 1996), emotional states at workplace are determined by the occurrence of discrete work events, especially the everyday problems and uplifts that everybody experience in their working lives. Basch and Fisher (2000) have demonstrated that employees experience almost the full range of emotions in their workplace, together with emotions that are specific to their work environment. They have also demonstrated that emotions in the workplace are tied to specific work events. The discrete emotions approach has identified basic or primary emotions, including joy, love, anger, fear, sadness, disgust, and surprise, each with a unique set of prototypical antecedents and consequences (Ekman, 1992).Studies suggest that positive emotional state is associated with positive outcomes in organizations, including better job satisfaction (Connolly & Viswesvaran, 2000), less turnover (Shaw, 1999), more helping behaviors (Williams & Shiaw, 1999), and improved performance (Cropanzano & Wright, 2001). The dominant hypothesis about employee emotions is that happy workers ought to be productive workers (Staw, Bell, & Clausen, 1986). Affect is critical to explaining outcomes that concern managers in organizations. It has effects on performance, decision making, turnover, pro-social behavior, negotiation and conflict resolution behavior, and group dynamics. Affect permeates virtually every aspect of organizational life, even those areas that have been traditionally thought of as the exclusive province of cognitive behavior, such as decision making and task performance. When employees are unhappy, it shows in organizational behavior, because unhappy people are disconnected from their work (Frost, 2003).It is believed that synthesizing various individual-level, group-level and organization-level variables through a model would be the logical step to understand their complex relationships with emotions in workplace. In this section, the theoretical underpinnings of the proposed model have been developed. For this purpose, an extensive literature review has been done to identify variables and the relationships among the variables. A conceptual framework has been developed incorporating all the variables. Abrief description of these variables and their relationships are given below.Individual level antecedent factorsSocial relations: Bourdieu (1997) states that social capital is the sum of the resources that accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition. …

E-Mail Fraud Detection - An Organisational Semiotic View

Deficient safety occurrence reporting by air traffic controllers is an important issue in many air traffic organizations. To understand the reasons for not reporting, practitioners formulated a number of hypotheses, which are difficult to verify manually. To perform automated, formally-based verification of the hypotheses an agent-based modeling and simulation approach is proposed in this paper. This approach allows modeling both institutional (prescriptive) aspects of the formal organization and social behavior of organizational actors. To our knowledge, agent-based organization modeling has not been attempted in air traffic previously. Using such an approach four hypotheses related to consequences of controller team composition in particular organizational contexts were examined.

We investigate the synthesis of policies for high-level agent programs expressed in Golog, a language based on situation calculus that incorporates nondeterministic programming constructs. Unlike traditional approaches for program realization that assume full agent control or rely on incremental search, we address scenarios where environmental nondeterminism significantly influences program outcomes. Our synthesis problem involves deriving a policy that successfully realizes a given Golog program while ensuring the satisfaction of a temporal specification, expressed in Linear Temporal Logic on finite traces (LTLf), across all possible environmental behaviors. By leveraging an expressive class of first-order action theories, we construct a finite game arena that encapsulates program executions and tracks the satisfaction of the temporal goal. A game-theoretic approach is employed to derive such a policy. Experimental results demonstrate this approach's feasibility in domains with unbounded objects and non-local effects. This work bridges agent programming and temporal logic synthesis, providing a framework for robust agent behavior in nondeterministic environments.

The growth of large‐scale neuroimaging collaborations has opened new opportunities for analyzing data at an unprecedented scale, driving the development of methods that exploit data variation to deepen our understanding of both healthy population variation and disease‐specific patterns. Among these methods, normative modeling stands out as a powerful framework that relates individual neuroimaging data to population‐derived standards. This approach models an individual's neuroimaging phenotypes in relation to demographic and clinical variables within a large, healthy population, enabling subject‐level inferences about their position relative to the population.While normative models excel in establishing robust population norms and identifying disease‐specific patterns, their potential for analyzing temporal dynamics has yet to be fully realized. The cross‐sectional nature of most datasets limits the ability to model subject‐level trajectories over time. Furthermore, existing methods for analyzing longitudinal data often fail to integrate insights from large‐scale cross‐sectional datasets, hindering the detection of early markers of neurodegeneration or subtle deviations from typical aging trajectories.To address this limitation, we introduce an innovative normative modeling approach that leverages the heterogeneity in large cross‐sectional datasets and also incorporates longitudinal dynamics for subject‐specific inferences. This method builds upon pre‐trained normative models derived from over 58,000 individuals, offering scalability, flexibility, and applicability across studies with varying sample sizes. By comparing an individual's trajectory with the expected dynamics derived from population norms, our approach enables the detection of atypical changes that might otherwise go unnoticed. The result of this method is an individualized score, which quantifies whether a significant change has occurred in each individual over time, allowing for more precise detection of deviations from typical trajectories.This technique holds particular promise for Alzheimer's disease research, where early detection and tracking of disease progression are critical. By integrating cross‐sectional and longitudinal data, our framework improves the ability to detect subtle changes over time, contributing to a more nuanced understanding of neurodegeneration. Furthermore, this approach is versatile and can be extended to other neurological and psychiatric conditions, providing a powerful tool for analyzing longitudinal neuroimaging data across a wide range of studies.