Goal setting mechanism in Petri net models of human decision making

Rauterberg, Matthias; Fjeld, Morten; Schluep, S Samuel

doi:10.1109/icsmc.1997.635345

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Fogel and Moore [30] modeled steering behavior (i.e., subconscious control behavior) of pilots in aeronautics using a state transition graph combined with evolutionary algorithms. Rauterberg et al [31] developed a Petri-net model of cognitive behavior in human-computer interaction. Human decision-making is modeled based on a log file of someone using particular software, i.e.…”

Section: Simulation Approaches Based On Finite State Machinesmentioning

confidence: 99%

Including Human Behavior in Product Simulations for the Investigation of Use Processes in Conceptual Design: A Survey

Vegte

́th

2006

Volume 3: 26th Computers and Information in Engineering Conference

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In this paper, approaches for behavioral simulation of humans and human-artifact systems are reviewed The objective was to explore available knowledge for the development of a new method and system for the simulation of use processes of consumer durables in conceptual design. A key issue is to resolve the trade-off between minimizing the modeling and computing effort on the one hand, and maximizing the amount of valuable information obtained from simulations to facilitate improving the product. After drawing up review criteria, we reviewed existing simulation approaches, which we characterized based on the simulation models. We found that the surveyed approaches can only address limited, largely unconnected subsets of the various behaviors that can be simulated. For the most advanced approaches, the subsets can be clustered into three main groups: (i) kinematics and rigid-body kinetics simulated with non-discretized object models, (ii) mechanical-deformation behavior and non-mechanical physical behavior simulated with discretized object models and (iii) interpreted physical behavior (information processing) simulated with finite-state machines. No clear-cut solutions for integrated behavioral simulation of use processes have been found, however, we could identify opportunities to bridge the gaps between the three groups of behavior, which can help us to resolve the aforementioned trade-off. In the first place, it seems that the possibilities for using discretized models in kinematics simulation (especially with consideration of the large deformations that are common in biomechanics) have not been fully explored. Alternatively, a completely new uniform modeling paradigm, possibly based on particles, might also help to resolve the gap between the two distinct groups of physical behaviors. Finally, hybrid simulation techniques can bridge the gap between the observed physical behaviors and interpreted physical behaviors. Here, the combination with the object models commonly used for simulations in group (i) and (ii) seems to be largely unexplored. Our findings offered valuable insights as a starting point for developing an integrated method and system for modeling and simulating use processes. We expect that other researchers dealing with similar issues in combining seemingly disconnected simulation approaches could benefit as well.

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Section: Simulation Approaches Based On Finite State Machinesmentioning

confidence: 99%