Modeling agent organizations using roles

Agent-based and active object systems are no longer contained within the boundaries of a single, small organization. To meet the demands of large-scale software and systems modeling, we need useful analogies for modeling and constructing large-scale systems of autonomous, interactive software entities. In this paper, we employ social and organizational systems theory as a way to guide our understanding of the notion of role and its implications on how agents (and active objects) might behave in group settings.     

Building dynamic agent organizations in cyberspace

The Karma-Teamcore framework focuses on rapidly integrating distributed, heterogeneous agents and tasking them via an abstract team-oriented program. The framework provides wrappers that encapsulate general teamwork reasoning and automatically generate the necessary coordination for robust execution of this abstract program. We describe the Karma-Teamcore framework and present an example of its successful application, namely, the simulated evacuation of civilians stranded in a hostile area     

An adaptive framework for monitoring agent organizations

Multiagent technologies are usually considered to be suitable for constructing agent organizations that are capable of running in dynamic and distributed environments and that are able to adapt to changes as the system runs. The necessary condition for this adaptation ability is to make agents aware of significant changes in both the environment and the organization. This paper presents mechanism, which helps agents detecting adaptation requirements dynamically at run time, and an Trace&Trigger, which is an adaptation framework for agent organizations. It consists of an event-tracing-based monitoring mechanism that provides organizational agents with information related to the costs and benefits of carrying out an adaptation process at each moment of the execution. This framework intends to overcome some of the problems that are present in other approaches by allowing the dynamic specification of the information that has to be retrieved by each agent at each moment for adaptation deliberation, avoiding the transference of useless information for adaptation deliberation. This framework has been integrated in the Magentix2 multiagent platform. In order to test its performance benefits for any agent organization, an example based on a market scenario is also presented.     

Social patterns in productive software development organizations

Software development is a predominantly social activity. It is important to view software development groups, departments, and corporations as social bodies. We study software organizations using a novel data-gathering approach that combines several techniques commonly used in social network analysis. Our techniques differ from those of ordinary social anthropology in that we help the organization introspect about itself; the technique is a mirror for the subject organization. We catalogued social network diagrams using a variety of visualization techniques. We have found visual patterns that correlate well to subjective measures of a good organization. We built analytical models to capture properties of the social networks, employing techniques similar to those used in established social network science. The emerging design technique based on architectural patterns provides a good vehicle for communicating organizational patterns. We have captured practices from outstanding organizations in a group of patterns that form a pattern language for productive software development.     

Quantifying patterns of agent–environment interaction

This article explores the assumption that a deeper (quantitative) understanding of the information-theoretic implications of sensory–motor coordination can help endow robots not only with better sensory morphologies, but also with better exploration strategies. Specifically, we investigate by means of statistical and information-theoretic measures to what extent sensory–motor coordinated activity can generate and structure information in the sensory channels of a simulated agent interacting with its surrounding environment. The results show how the usage of correlation, entropy, and mutual information can be employed (a) to segment an observed behavior into distinct behavioral states; (b) to analyze the informational relationship between the different components of the sensory–motor apparatus; and (c) to identify patterns (or fingerprints) in the sensory–motor interaction between the agent and its local environment.     

Evolutionary approach for integration of multiple pronunciation patterns for enhancement of dysarthric speech recognition

Dysarthria is a motor speech disorder caused by neurological injury of the motor component of the motor-speech system. Because it affects respiration, phonation, and articulation, it leads to different types of impairments in intelligibility, audibility, and efficiency of vocal communication. Speech Assistive Technology (SAT) has been developed with different approaches for dysarthric speech and in this paper we focus on the approach that is based on modeling of pronunciation patterns. We present an approach that integrates multiple pronunciation patterns for enhancement of dysarthric speech recognition. This integration is performed by weighting the responses of an Automatic Speech Recognition (ASR) system when different language model restrictions are set. The weight for each response is estimated by a Genetic Algorithm (GA) that also optimizes the structure of the implementation technique (Metamodels) which is based on discrete Hidden Markov Models (HMMs). The GA makes use of dynamic uniform mutation/crossover to further diversify the candidate sets of weights and structures to improve the performance of the Metamodels. To test the approach with a larger vocabulary than in previous works, we orthographically and phonetically labeled extended acoustic resources from the Nemours database of dysarthric speech. ASR tests on these resources with the proposed approach showed recognition accuracies over those obtained with standard Metamodels and a well used speaker adaptation technique. These results were statistically significant.     

Intelligent agent construction using the attentive characteristic patterns of chaotic neural networks

Humans learn from incidents in their own life and reflects these in subsequent actions as their own experiences. These experiences are memorized in the brain and recollected when necessary. This research incorporates this type of intelligent information processing mechanism and applies it to an autonomous agent. In the proposed system, the reinforcement Q-learning method is used. Autoassociative chaotic neural networks are also used as mutual associative memory systems. However, an agent cannot retrieve all stored patterns exactly, especially in the case of too many stored patterns and a strong correlation among them. To solve this problem, we propose to use types of attentive parameters and attentive characteristic patterns. The attentive characteristic pattern is part of the stored patterns. When robots concentrate their attention on a specific part of a stored pattern, i.e., the attentive characteristic pattern, whole stored patterns are retrieved easily and completely. Finally, the effectiveness of the proposed method is verified through a simulation applied to plural maze-searching problems.     

Evolutionary design of Evolutionary Algorithms

Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.     

Communications architecture: towards a more robust understanding of information flows and emergent patterns of communication in organizations

With the proliferation of telecommunications technologies, the information-based communication infrastructure is becoming an increasingly critical organization resource. In order effectively to channel limited resources (skills, capital, technology) to the most strategically critical communication needs of the organization, the development of business driven planning methodologies which result in a well-defined architecture (blueprint) of organizational communication processes are needed. Unfortunately, while architectural issues are of utmost importance today, researchers have focused almost exclusively on data oriented models. This study attempts to expand this view and provide a holistic representation of information architecture. With the perspective provided by this definitional framework, two methods for development of communications architecture are discussed and evaluated: (1) a flow based approach; and (2) network analysis. Network analysis in particular shows great promise in constructing robust representations of organizational communication processes.     

演化密码与DES的演化研究

安全密码的设计是十分复杂、困难的．密码的高安全性和密码设计自动化是人们长期追求的目标．该文将密码学与演化计算结合起来，借鉴生物进化的思想，提出演化密码的概念和用演化计算设计密码的方法，以DES密码为例，实际获得了成功，演化设计得到一批比DES原S盒好的S盒．而且以这种渐强的S盒为基础构成了演化DES密码系统．这些研究工作无论是对密码学还是对演化计算都是十分有意义的．     

Effects of CSCW on organizations

We consider the potential impact of Computer Supported Cooperative Work, with special reference to large technically advanced projects involving several organizations. It is vital that such projects are managed efficiently, without delays, since a product that reaches the market a few months earlier than its competitors enjoys a great advantage. Traditional methods of coordinating large projects, based on hierarchical communication, tend to produce delays, since technicians at remote sites are obliged to solve coordination problems by passing them up the hierachy. It would be better if such problems were solved by improvising conferences among the technicians; Computer Supported Cooperative Work will provide the technical means of implementing this heterarchical style of management without losing control of the project. The use of computers as a social medium raises methodological and ethical issues which are discussed in the final section.     

Model management in organizations

The premise that the personal computer/spreadsheet explosion will result in the evolution of model management within organizations is explored. The authors use Nolan's stage model of organizational data processing activity as a basis for discussing the nature of change in organizations as local computing capability proliferates. The mainframe era resulted in the recognition of data as a resource and gave rise to data administration. The authors expect that the personal computer era and the accompanying spreadsheet explosion will lead to the recognition of models as a valuable and manageable resource. The role of model administration within organizations is discussed as are software tools for supporting this functional activity. The information resource encyclopedia, an extension of the traditional data dictionary concept, and the model management system are introduced as integral components for supporting model administration. An example is presented to suggest an integrative approach for implementing an MMS in a spreadsheet environment.     

Evolutionary triangulator

This paper describes a new approach to automatic generation of unstructured and irregular triangulations of convex regions. Such triangulations find many applications, including solutions of partial differential equations using the finite element methods. A method that is based on the ideas of genetic algorithms and overcomes some weaknesses of the prior geometric approaches is presented. In general, a genetic algorithm allows a solution to evolve by selecting and promoting better solution candidates over the worse candidates. It is a self-learning algorithm enforced by raising the average fitness value of the population as the generations go by. The method starts with a random population of candidate solution, which admits a low quality of triangles and violation of the conformance criterion. The triangulator then quickly learns to move any ''nonconforming'' points inside the boundary, and continues with focusing on ''teaching'' the population to favor good quality triangular shapes as the generations evolve.     

Evolutionary Fervor

The business community has long embraced the notion that progress is an evolutionary process that comes with markets, competition, and the survival of the fittest.     

Evolution of information systems in organizations

Of all organizational phenomena and management techniques, the use of computers and data processing have been perhaps the most distinct and penetrating. The steady progress of technology and common patterns of growth have inspired a whole body of literature on the evolution of information systems. This article reviews the relevant literature and the theoretical underpinnings of different views. We develop a framework which includes elements from economics, diffusion theories, organizational learning and growth and stages theory. Within this framework we integrate different views on the subject and look at what broader theoretical features are applied in the models presented in information systems research. On the basis of the review, we draw some conclusions about the limitations of the models and the need for future research in the field.     

Social media information governance in multi-level organizations: How humanitarian organizations accrue social capital

Strategic social media use positively influences organizational goals such as the long-term accrual of social capital, and thus social media information governance has become an increasingly important organizational objective. It is particularly important for humanitarian nongovernmental organizations (HNGOs), whose work relies on accurate and timely information regarding socially altruistic behavior (donations, volunteerism, etc.). Despite the potential of social media for increasing social capital, tensions in governing social media information across an organization's different operational levels (regional, intermediate, and national) pose a difficult challenge. Prominent governance frameworks offer little guidance, as their focus on control and incremental policymaking is largely incompatible with the processes, roles, standards, and metrics needed for managing self-governing social media. This study offers a notion of dynamic and co-evolutionary process management of multi-level organizations as a means of conceptualizing social media information governance for the accrual of organizational social capital. Based on interviews with members of HNGOs, this study reveals tensions that emerge within eight focus areas of accruing social capital in multi-level organizations, explains how dynamic process management can ease those tensions, and proposes corresponding strategy recommendations.