3 research outputs found
Evolving Models From Observed Human Performance
To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behavior. This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show not only the ability/capability to automatically learn and generalize the behavior of the human observed, but they also capture some of the personal behavior patterns observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer
Some aspects of human performance in a Human Adaptive Mechatronics (HAM) system
An interest in developing the intelligent machine system that works in conjunction with
human has been growing rapidly in recent years. A number of studies were conducted to
shed light on how to design an interactive, adaptive and assistive machine system to
serve a wide range of purposes including commonly seen ones like training,
manufacturing and rehabilitation. In the year 2003, Human Adaptive Mechatronics
(HAM) was proposed to resolve these issues. According to past research, the focus is
predominantly on evaluation of human skill rather than human performance and that is
the reason why intensive training and selection of suitable human subjects for those
experiments were required. As a result, the pattern and state of control motion are of
critical concern for these works.
In this research, a focus on human skill is shifted to human performance instead due to
its proneness to negligence and lack of reflection on actual work quality. Human
performance or Human Performance Index (HPI) is defined to consist of speed and
accuracy characteristics according to a well-renowned speed-accuracy trade-off or
Fitts’ Law. Speed and accuracy characteristics are collectively referred to as speed and
accuracy criteria with corresponding contributors referred to as speed and accuracy
variables respectively. This research aims at proving a validity of the HPI concept for
the systems with different architecture or the one with and without hardware elements.
A direct use of system output logged from the operating field is considered the main
method of HPI computation, which is referred to as a non-model approach in this thesis.
To ensure the validity of these results, they are compared against a model-based
approach based on System Identification theory. Its name is due to being involved with
a derivation of mathematical equation for human operator and extraction of
performance variables. Certain steps are required to match the processing outlined in
that of non-model approach. Some human operators with complicated output patterns
are inaccurately derived and explained by the ARX models
Some aspects of human performance in a Human Adaptive Mechatronics (HAM) system
An interest in developing the intelligent machine system that works in conjunction with human has been growing rapidly in recent years. A number of studies were conducted to shed light on how to design an interactive, adaptive and assistive machine system to serve a wide range of purposes including commonly seen ones like training, manufacturing and rehabilitation. In the year 2003, Human Adaptive Mechatronics (HAM) was proposed to resolve these issues. According to past research, the focus is predominantly on evaluation of human skill rather than human performance and that is the reason why intensive training and selection of suitable human subjects for those experiments were required. As a result, the pattern and state of control motion are of critical concern for these works. In this research, a focus on human skill is shifted to human performance instead due to its proneness to negligence and lack of reflection on actual work quality. Human performance or Human Performance Index (HPI) is defined to consist of speed and accuracy characteristics according to a well-renowned speed-accuracy trade-off or Fitts' Law. Speed and accuracy characteristics are collectively referred to as speed and accuracy criteria with corresponding contributors referred to as speed and accuracy variables respectively. This research aims at proving a validity of the HPI concept for the systems with different architecture or the one with and without hardware elements. A direct use of system output logged from the operating field is considered the main method of HPI computation, which is referred to as a non-model approach in this thesis. To ensure the validity of these results, they are compared against a model-based approach based on System Identification theory. Its name is due to being involved with a derivation of mathematical equation for human operator and extraction of performance variables. Certain steps are required to match the processing outlined in that of non-model approach. Some human operators with complicated output patterns are inaccurately derived and explained by the ARX models.EThOS - Electronic Theses Online ServiceGBUnited Kingdo