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Siebers, Peer-Olaf; Aickelin, Uwe; Celia, Helen; Clegg, Chris W.
doi: 10.1177/0037549708101575pmid: N/A
Agents offer a new and exciting way of understanding the world of work. In this paper we describe the development of agent-based simulation models, designed to help to understand the relationship between people management practices and retail performance. We report on the current development of our simulation models which includes new features concerning the evolution of customers over time. To test the features we have conducted a series of experiments dealing with customer pool sizes, standard and noise reduction modes, and the spread of customers’ word of mouth. To validate and evaluate our model, we introduce new performance measure specific to retail operations. We show that by varying different parameters in our model we can simulate a range of customer experiences leading to significant differences in performance measures. Ultimately, we are interested in better understanding the impact of changes in staff behavior due to changes in store management practices. Our multi-disciplinary research team draws upon expertise from work psychologists and computer scientists. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents offer potential for fostering sustainable organizational capabilities in the future.
Smith, Anton H. C.; Ponci, Ferdinanda; Monti, Antonello
doi: 10.1177/0037549708101942pmid: N/A
Parametric uncertainty can represent parametric tolerance, parameter noise or parameter disturbances. The effects of these uncertainties on the time evolution of a system can be extremely significant, mostly when studying closed-loop operation of control systems. The presence of uncertainty makes the modeling process challenging, since it is impossible to express the behavior of the system with a deterministic approach. If the uncertainties can be defined in terms of probability density function, probabilistic approaches can be adopted. In many cases, the most useful aspect is the evaluation of the worst-case scenario, thus limiting the problem to the evaluation of the boundary of the set of solutions. This is particularly true for the analysis of robust stability and performance of a closed-loop system. The goal of this paper is to demonstrate how the polynomial chaos theory (PCT) can simplify the determination of the worst-case scenario, quickly providing the boundaries in time domain. The proposed approach is documented with examples and with the description of the Maple worksheet developed by the authors for the automatic processing in the PCT framework.
Chen, Chih-Chun; Clack, Christopher D.; Nagl, Sylvia B.
doi: 10.1177/0037549709106692pmid: N/A
Agent-directed simulations (ADS) are used in many domains to study complex systems. These are systems where non-linear effects can result from these emergent behaviors, making them difficult to analyze and predict. Correspondingly, in ADS, as well as explicitly specified behaviors of individual agents, higher level behaviors can emerge spontaneously from agent action sequences and agent—agent interactions. We have previously introduced the complex event formalism for specifying emergent behaviors in dynamically executing ADS [1, 2]. Based on the formalism, we also described a method for detecting and analyzing emergent behaviors in multi-agent simulations, giving us an effective means of studying, and a more reliably way of predicting, these systems. Complex event types define sets of multi-dimensional structures of interrelated events arising from the actions of one or more agents. They are therefore directly related to the agent specifications, which determine the behavior of individual agents. Although the abstract constructs of the formalism have already been introduced in [1] and [2], they have not yet been related to a specific agent-based specification language. Here, we define the constructs in terms of the X-machine formalism, which is widely used to specify multi-agent systems. This extends the existing X-machine framework to model higher level emergent behaviors as well as agent-level state transitions. Thus, emergent behaviors at any level of abstraction can be specified for detection and analysis in a dynamically executing ADS.
Vieira, Maria F. Q.; Neto, José A. N.; Scaico, Alexandre; Santoni, Charles; Mercantini, Jean-Marc
doi: 10.1177/0037549709346281pmid: N/A
Operator training systems are essential tools for industrial systems, particularly for those where human error has an impact on the safety of materials and personnel and/or may cause significant financial losses. The work presented here is part of a major research project concerned with the study of operator behavior when facing safety-critical situations. The application concerns the supervisory control of an electricity distribution substation. This paper focuses on the first phase of the project; the development of a real-time operator training system that simulates the supervisory control system. The simulator promotes the operator’s immersion into a virtual replica of the real working environment. The simulator’s architecture, based upon a set of formal models interconnected to form the simulation engine, is presented. The models were constructed using the colored Petri nets formalism. The modular architecture allows for remote interaction via the web and offers two interfaces with the plant control system: a virtual reality representation of the human—machine interface and a supervisory system representation. This paper presents and discusses the architecture of the Operator Training Simulator.
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