On capturing human skills and knowledge; algorithmic approaches to model identification

Rouse, William B.; Hammer, John M.; Lewis, Charles

doi:10.1109/21.31062

Cited by 35 publications

(9 citation statements)

References 52 publications

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“…Given x(t), u(t), s(t) for t = 1,2, ..., one can identify F' and H using one or more of a variety of identification algorithms (Rouse, et al, 1989). If relationship (1) represents a signal processing task (e.g., flight control), identification of F' and H should be reasonably straightforward.…”

mentioning

confidence: 99%

Modeling the dynamics of mental workload and human performance in complex systems

Rouse

Edwards

Hammer

1993

IEEE Trans. Syst., Man, Cybern.

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REPORT DOCUMEr7_ATION PAGEOMB No 0104oa01 "*ý4bolc eioOrtinc oufoen or tn:s collection of inform ation is estim i•v, to average I hour per resipore. ninuairg tpe time i. r-•e-eriq instruCtionS. Sear(hrnq ex ,st•,r cata %o•iice.gathering ano maintaining the data needed, and completing ano reviewing the collectiOn of intorfmatio.Send comments rPiarching this burden estimate or any otner aspect of this collection of information. including suggestions for reducing this ourden. ABSTRACT (Maximum 200 words)This program studied the relationship between subjective workload and human behavior and proposed a model of the dynamics of this relationship. Results of three simulation experiments are detailed in this report and show that simple linear indentification algorithms are robust in online identification of noisy, nonlinear versions of the model. This model and the associated algorithms have the potential to enable online inferences of workload and could be used to prompt/invoke human aiding or automated systems to help reduce workload.Applications for such systems exist in aiding aircraft pilots; command, control, communication decision makers; and other personnel in dynamic, time constrained environments.14. SO M bl9, HUHAN PERFORHANCE, PILOT AIDING. workload to acceptable levels.

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confidence: 99%

Modeling the dynamics of mental workload and human performance in complex systems

Rouse

Edwards

Hammer

1993

IEEE Trans. Syst., Man, Cybern.

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“…They argued that the use of a mental model construct may enable the development of a better understanding of such global team-related phenomena as coordination and communication performance. Rouse et al [20] have reviewed and illustrated the algorithmic identi®cation of models of human skills and knowledge. To review a variety of approaches to training and reinforce mental models, see Campbell [21] and Kieras [22].…”

Section: Introductionmentioning

confidence: 99%

Forming teams: an analytical approach

1999

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The selection of multi-functional teams is a key issue in problem solving. Currently there are no papers in the literature that discuss analytical approaches to forming teams. Furthermore, no comprehensive model exists to prioritize team membership based on customer requirements or product characteristics. To deal with the underlying complexities of the team selection process, a methodology for team formation is developed. The methodology is based on the Analytical Hierarchy Process (AHP) approach and the Quality Function Deployment (QFD) method. A QFD planning matrix is used to organize the factors considered in the team selection. The importance measure for each team member is determined with the AHP approach. A mathematical programming model is developed to determine the composition of a team. The methodology developed in this paper is tested by the selection of teams in concurrent engineering. A detailed discussion of the model implementation and how to reduce the number of comparisons in the AHP process is presented. Possible modi®cations of the model to include``soft factors'', i.e., leadership, morale, personalities of group members, group values and so on are also discussed.

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“…Although Luczak (1975) and Fisher et al (1981) made early attempts to introduce descriptive mathematical models based on automata theory and Markov Chains, the majority of models are still prescriptive (see survey of cognitive models in Luczak 1997) and, therefore, only have a limited validity for the dynamic individualization of user interfaces. If we focus on descriptive models, a survey of Rouse et al (1989) reviews a few symbolic approaches, but they deal with a deterministic behaviour description with the help of switching functions, ®nite-state machines or pushdown automata. Unfortunately, these approaches do not consider the well-established methodology of the science of stochastic processes and, therefore, cannot cope with behavioural uncertainty due to individually preferred interaction paths in the redundant state space of direct manipulation displays or due to human errors.…”

Section: Syntactic User Modellingmentioning

confidence: 99%

Syntactic user modelling with stochastic processes

Luczak¹,

Schlick²,

Kuenzer³

et al. 2001

Theoretical Issues in Ergonomics Science

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An approach to user modelling with discrete stochastic processes is presented, which aims at the dynamic individualization of user interfaces on the syntactic layer. The state of the art of syntactic user modelling is surveyed. The mathematical background of simple Markov Chains and the`classic' Hidden Markov Model is presented. Furthermore, dynamic Bayesian Networks are introduced, which generalize these Markovian Models. A case study of the simulation experiments uses a multimodal user interface for supervisory control of advanced manufacturing cells. A corresponding simulation model is created and exploited to generate interaction cases, which are the empirical basis for the evaluation. Six topologies of dynamic Bayesian Networks are evaluated for 100 interaction cases and 50 replications each: (1) Markov Chain of order 1, (2) Hidden Markov Model, (3) autoregressive Hidden Markov Model, (4) factorial Hidden Markov Model, (5) simple hierarchical Hidden Markov Model, and (6) tree structured Hidden Markov Model. The dependent variable is the prediction accuracy for a single prediction lead. In a ®rst step, a one-way analysis of variance in conjunction with Tukey's post-hoc test demonstrated a signi®cant superiority of the simple hierarchical Hidden Markov Model. In a second step, an additional two-way analysis of variance also indicated a signi®cantly better prediction accuracy of the simple hierarchical Hidden Markov Model compared to the Hidden Markov Model, but the number of interaction cases also had a signi®cant eVect. Hence, the modeller has to take both factorsÐmodel topology and number of interaction casesÐinto account when designing syntactic user models with stochastic processes. IntroductionThis study deals with theoretical aspects of software ergonomic design of human± machine systems. According to the ISO 9241 standard (part 10), seven ergonomic principles of dialogue design are distinguished. In the following sections, the sixth design principle`suitability for individualization' is analysed in detail. The suitability for individualization deals with adapting structure and dynamics of the user interface to human information processing. With regard to Rasmussen's (1986) model of human information processing, three levels of cognitive control can be taken into account: skill-based behaviour, rule-based behaviour, and knowledge-base d behaviour. We focus on a dynamic individualizatio n shaped by rule-based behaviour. From the point of view of semiotics (Morris 1938), the rule-based behaviour

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On capturing human skills and knowledge; algorithmic approaches to model identification

Cited by 35 publications

References 52 publications

Modeling the dynamics of mental workload and human performance in complex systems

Modeling the dynamics of mental workload and human performance in complex systems

Forming teams: an analytical approach

Syntactic user modelling with stochastic processes

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