${\cal GISM}$ : A Language for Modelling and Designing Agent-Based Intelligent Systems

1998 ◽  
pp. 122-136 ◽  
Author(s):  
Hongxue Wang ◽  
John Slaney
Keyword(s):  
Intelligent Systems ◽  
Agent Based

scholarly journals Escape from the factory of the robot monsters: agents of change

2017 ◽  
Vol 23 (1/2) ◽  
pp. 96-108 ◽  
Author(s):  
Dale Richards
Keyword(s):  
Human Computer Interaction ◽  
Human Factors ◽  
Intelligent Systems ◽  
Autonomous Agents ◽  
Content Type ◽  
Team Members ◽  
Agent Based ◽  
Delegation Of Authority ◽  
Human Agent ◽  
Computer Interaction

Purpose The increasing use of robotics within modern factories and workplaces not only sees us becoming more dependent on this technology but it also introduces innovative ways by which humans interact with complex systems. As agent-based systems become more integrated into work environments, the traditional human team becomes more integrated with agent-based automation and, in some cases, autonomous behaviours. This paper discusses these interactions in terms of team composition and how a human-agent collective can share goals via the delegation of authority between human and agent team members. Design/methodology/approach This paper highlights the increasing integration of robotics in everyday life and examines the nature of how new novel teams may be constructed with the use of intelligent systems and autonomous agents. Findings Areas of human factors and human-computer interaction are used to discuss the benefits and limitations of human-agent teams. Research limitations/implications There is little research in (human–robot) (H–R) teamwork, especially from a human factors perspective. Practical implications Advancing the author’s understanding of the H–R team (and associated intelligent agent systems) will assist in the integration of such systems in everyday practices. Social implications H–R teams hold a great deal of social and organisational issues that need further exploring. Only through understanding this context can advanced systems be fully realised. Originality/value This paper is multidisciplinary, drawing on areas of psychology, computer science, robotics and human–computer Interaction. Specific attention is given to an emerging field of autonomous software agents that are growing in use. This paper discusses the uniqueness of the human-agent teaming that results when human and agent members share a common goal within a team.

PAHIS: A Platform for Agent-Based Hybrid Intelligent Systems

2013 ◽  
Vol 462-463 ◽  
pp. 915-919
Author(s):  
Chun Sheng Li ◽  
Kan Li
Keyword(s):  
Intelligent Systems ◽  
Agent Based ◽  
Hybrid Intelligent Systems ◽  
Virtual Organisation ◽  
Group Roles ◽  
Architectural Model ◽  
Run Time

For solving the dynamic addition and removal problems of group-based agents at run-time, PAHIS is developed by analysis and mplementation. Category role, group roles, virtual organisation role, and dynamics rules are conducted in analysis. A self-organising ring-based architectural model has been employed to organise middle agents in implementation. PAHIS is implemented by using C and Socket and can be used as an infrastructure of agent-based hybrid intelligent systems.

Agent-Based Intelligent System Modeling

2011 ◽  
pp. 51-57
Author(s):  
Zaiyong Tang ◽  
Xiaoyu Huang ◽  
Kallol Bagchi
Keyword(s):  
Complex Systems ◽  
System Dynamics ◽  
Intelligent Agents ◽  
Intelligent Systems ◽  
Intelligent System ◽  
System Modeling ◽  
Agent Based Modeling ◽  
Changing Environment ◽  
Agent Based ◽  
Sense And Respond

An intelligent system is a system that has, similar to a living organism, a coherent set of components and subsystems working together to engage in goal-driven activities. In general, an intelligent system is able to sense and respond to the changing environment; gather and store information in its memory; learn from earlier experiences; adapt its behaviors to meet new challenges; and achieve its pre-determined or evolving objectives. The system may start with a set of predefined stimulusresponse rules. Those rules may be revised and improved through learning. Anytime the system encounters a situation, it evaluates and selects the most appropriate rules from its memory to act upon. Most human organizations such as nations, governments, universities, and business firms, can be considered as intelligent systems. In recent years, researchers have developed frameworks for building organizations around intelligence, as opposed to traditional approaches that focus on products, processes, or functions (e.g., Liang, 2002; Gupta and Sharma, 2004). Today’s organizations must go beyond traditional goals of efficiency and effectiveness; they need to have organizational intelligence in order to adapt and survive in a continuously changing environment (Liebowitz, 1999). The intelligent behaviors of those organizations include monitoring of operations, listening and responding to stakeholders, watching the markets, gathering and analyzing data, creating and disseminating knowledge, learning, and effective decision making. Modeling intelligent systems has been a challenge for researchers. Intelligent systems, in particular, those involve multiple intelligent players, are complex systems where system dynamics does not follow clearly defined rules. Traditional system dynamics approaches or statistical modeling approaches rely on rather restrictive assumptions such as homogeneity of individuals in the system. Many complex systems have components or units which are also complex systems. This fact has significantly increased the difficulty of modeling intelligent systems. Agent-based modeling of complex systems such as ecological systems, stock market, and disaster recovery has recently garnered significant research interest from a wide spectrum of fields from politics, economics, sociology, mathematics, computer science, management, to information systems. Agent-based modeling is well suited for intelligent systems research as it offers a platform to study systems behavior based on individual actions and interactions. In the following, we present the concepts and illustrate how intelligent agents can be used in modeling intelligent systems. We start with basic concepts of intelligent agents. Then we define agent-based modeling (ABM) and discuss strengths and weaknesses of ABM. The next section applies ABM to intelligent system modeling. We use an example of technology diffusion for illustration. Research issues and directions are discussed next, followed by conclusions.

scholarly journals Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour

2019 ◽  
Author(s):  
Andrea Aler Tubella ◽  
Andreas Theodorou ◽  
Frank Dignum ◽  
Virginia Dignum
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Intelligent Systems ◽  
Deep Neural Networks ◽  
Well Being ◽  
Moral Values ◽  
Human Values ◽  
Agent Based ◽  
Multiple Domains ◽  
Inputs And Outputs

Artificial Intelligence (AI) applications are being used to predict and assess behaviour in multiple domains which directly affect human well-being. However, if AI is to improve people’s lives, then people must be able to trust it, by being able to understand what the system is doing and why. Although transparency is often seen as the requirement in this case, realistically it might not always be possible, whereas the need to ensure that the system operates within set moral bounds remains. In this paper, we present an approach to evaluate the moral bounds of an AI system based on the monitoring of its inputs and outputs. We place a ‘Glass-Box’ around the system by mapping moral values into explicit verifiable norms that constrain inputs and outputs, in such a way that if these remain within the box we can guarantee that the system adheres to the value. The focus on inputs and outputs allows for the verification and comparison of vastly different intelligent systems; from deep neural networks to agent-based systems. The explicit transformation of abstract moral values into concrete norms brings great benefits in terms of explainability; stakeholders know exactly how the system is interpreting and employing relevant abstract moral human values and calibrate their trust accordingly. Moreover, by operating at a higher level we can check the compliance of the system with different interpretations of the same value.

The Evolving Future of Agent-Based Electronic Commerce

2011 ◽  
pp. 337-350 ◽  
Author(s):  
T. Deshani Rodrigo ◽  
Peter A. Stanski
Keyword(s):  
Electronic Commerce ◽  
Business Model ◽  
Intelligent Systems ◽  
Integrated Systems ◽  
Mobile Code ◽  
Web Based ◽  
Agent Based ◽  
On Line ◽  
The Impact ◽  
New Millennium

E-commerce technologies are continually evolving, bringing about innovative developments and resultant benefits. Herein one such visionary path for existing on-line systems to adopt is presented. An emerging set of models is discussed which combines intelligent systems, mobile code applications (MCAs) and Web-based systems. Such technologies are presented to illustrate the impact upon the numerous new value-adds for users brought about by e-commerce vendors. These are discussed in context of current developments in related fields, to expose the full gains from the integrated systems synergy. Furthermore, we conclude with an expected business model for electronic commerce in the new millennium and beyond.

Total etch dentin bonding systems: scanning electron microscopy of the resin-dentin hybrid layer

1992 ◽  
Vol 50 (2) ◽  
pp. 1092-1093
Author(s):  
Jorge Perdigao
Keyword(s):  
Composite Resin ◽  
Mechanical Interlocking ◽  
Hybrid Layer ◽  
Agent Based ◽  
Dentin Bonding ◽  
Acid Agent ◽  
Bond Strengths ◽  
Main Component ◽  
Bonding Systems ◽  
New Generation

In 1955, Buonocore introduced the etching of enamel with phosphoric acid. Bonding to enamel was created by mechanical interlocking of resin tags with enamel prisms. Enamel is an inert tissue whose main component is hydroxyapatite (98% by weight). Conversely, dentin is a wet living tissue crossed by tubules containing cellular extensions of the dental pulp. Dentin consists of 18% of organic material, primarily collagen. Several generations of dentin bonding systems (DBS) have been studied in the last 20 years. The dentin bond strengths associated with these DBS have been constantly lower than the enamel bond strengths. Recently, a new generation of DBS has been described. They are applied in three steps: an acid agent on enamel and dentin (total etch technique), two mixed primers and a bonding agent based on a methacrylate resin. They are supposed to bond composite resin to wet dentin through dentin organic component, forming a peculiar blended structure that is part tooth and part resin: the hybrid layer.

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