Running Head: THE DEFINING CHARACTERS OF AIR MIDAS: AIR MAN-MACHINE
INTEGRATION DESIGN AND ANALYSIS SYSTEM
The Defining Characters of Air MIDAS:
Air Man-Machine Integration Design and Analysis System
Mersie A. Melke
Embry-Riddle Aeronautical University
Daytona Beach, Florida
Department of Distance Learning
Instructor: Susan Bailey-Schmidt
January 29, 2015
Defining Characters Air MIDAS 1
Humans have been using tools to enhance their outputs. This need to do better than the past
drove humans to the use of more complex machinery capable of achieving things impossible
to humans alone. However, as machines became more powerful and helpful to humans, the
relationship between the two entities began to have an agenda of its own. Consequently,
studying about man-machine relations is inevitable in current times. One tool that helps in
doing such studies is the Air MIDAS software. This paper shows the defining characters, the
requirements and possible outputs of this software. In addition, this paper addresses the
background on the topic of man-machine performance modeling as related to the Air MIDAS
Defining Characters Air MIDAS 2
Systems that function because of inputs from both humans and machines are results of
contemporary times. Both humans and machines bring a set of characters and behaviors in
the way they react, both to each other and to the work environment. Therefore, in addition to
having a comprehensive knowledge of the technical side of machines, having an
understanding of humans and their reaction to machine augmented systems has become an
issue that needs addressing. This is because enhancing the human-machine relationship is one
of the ways for optimizing output of a prospective system or a system with previous
experience, besides developing machines with capacity superior than their predecessors.
In studying the relationship between man and machine mentioned above, two methods
are of relevant value until now. One method involves humans as part of an experiment while
evaluating the characteristic of both the machine and the humans in the system. This method
is termed as the Human In The Loop (HITL) simulation. HITL simulations calls for
integration of independent elements (technologies, service providers and service receivers),
the logistics of whose coordination places a complexity and expense burden on the simulation
(Corker, Gore, 2001).
Another type of simulation is the Human Out Of The Loop (HOOTL) type of
simulation. HOOTL simulation is one that uses computer models of human performance as
the human agent interacts with new and/or simulated technologies and procedures. These
simulation tools are computer based simulation processes where human characteristics taken
from years of research from respective fields, embedded within a computer-generated
representation of humans’ operating environment, create a mockup of the real world. HOOTL
simulation is an alternative methodology to the expensive HITL simulations. One reason is
the ability to use HOOTL simulations at an earlier process in the development of a product,
system or technology (Corker, Gore, 2001).
Defining Characters Air MIDAS 3
Both methods are collectively termed as human performance modeling techniques.
Development of these methods is with the preconceived idea of deriving or anticipating
possible outcomes from a machine or machine augmented system that involves humans. As a
result, with having such kind of information, designers would be able to enhance their
prospective ideas to develop an optimal design. When thoughts of upgrading already
functioning systems arises, HITL and HOOTL methods can be employed to derive needed
information about the human-machine interface.
Background of Human Performance Modeling Techniques
Human performance modeling debuted over 50 years ago with quasi-linear and
manual control models. Human performance modeling in these times related to modeling
human tracking behavior in a closed-loop person-machine system (Gore, 1999). The reason
for the term quasi-linear stems from an engineer’s assumption of a linear transfer function
model for the operator’s control behavior in perceiving an error and translating this error to a
response (Gore, 1999).
In the aviation environment, human performance modeling initiated in the 1950s
(Foyle, Hooey, 2008). A person by the name Duane McRuer was interested in aircraft
handling qualities. Consequently, McRuer pioneered recasting the dynamics of flight
traditionally expressed in partial differential equations into control engineering transfer
function terms (Foyle, Hooey, 2008). The former is a relationship between several flight
variables that are interdependent on one another. Consequently, an outcome of a system
comprised of these variables will consider the changes of the variables in terms of one
another. The latter entails a workflow kind of relationship that has a certain set of input, a
certain set of analysis and a corresponding out put. However, McRuer came to a challenge of
representing human pilot in the system he tried to develop. As a result, McRuer set about to
explore the control engineering representation for what came to be called “manual control”—
Defining Characters Air MIDAS 4
a model of the dynamical response of the human controller (Foyle, Hooey, 2008). Manual
control was the dominant research drive to human performance modeling in aviation.
Eventually, cognitive architectures that focused on the higher level of human behavior in a
specific environment took over.
Basis for Air MIDAS
Air Man-Machine Integration Design and Analysis System (Air MIDAS) is one of the
different HOOTL tools employed to formulate an analysis of humans in a machine-
augmented systems. It is a joint development effort between the San Jose State University
and National Aeronautics Space Administration (NASA) Ames research center. Initially in
1984, the United States Army and NASA Ames research center had initiated a project to
come up with a modeling software that could analyze the characteristics of humans in a work
environment, the outcome of which was the Core MIDAS software (Gore, 1999).
The development effort of Core MIDAS software focused on the individual operator
in a psychological theory framework (Gore, 1999). Beginning in 1993, a parallel
development effort that led to the Air MIDAS software started. This focused on the team
aspect of human behavior in the framework of psychological theory (Gore, 1999).
Development of Air MIDAS was by NASA Ames research center and San Jose State
University, primarily for aviation related applications such as the examination of procedural
rule set changes on critical event recovery in the National Air Space (Gore, Corker, 2001).
The concept of human modeling in Air MIDAS bases on a psychological concept
referred to as First Principles. First Principles model of human performance bases on the
mechanisms that underlies and cause human behavior (Laughery, Corker, 1997). First
Principles model integrate human perceptual (data acquisition), cognitive (thought process)
and motor (physical manifestations) systems thus incorporating the high-level behaviors that
are characteristic of human performance. First Principles model also allow the human model
Defining Characters Air MIDAS 5
to learn about the system and recall about the system by integrating the theoretical and
practical models of human behavior in to the human model (AGARD, 1998). Consequently,
First Principles model of human performance provides models of emergent human behavior
based on elementary models of human behaviors such as perception, attention, working
memory, long-term memory and decision-making (Gore, Corker, 1999).
Defining Characters of Air MIDAS
Air MIDAS runs on a personal computer (PC) using the Windows 2000 or XP
operating system and requires Visual C++ 6.0, Alegro CL, and JAVA software (Foyle,
Hooey, 2008). The code for the software written in the LISP, C and C++ programming
languages, historically had 350,000 lines of code (Pew, Mavor, 1998).
The main components of the First Principles model, the building block of Air
MIDAS, comprise the simulated representation of the real world within which the agent
modeled by Air MIDAS exists. A symbolic operator model (SOM) that represents the
perceptual and cognitive activities of the agent exists. Another element of the SOM is the
Updateable World Representation (UWR). The world representation information
(environment, crew station, vehicle, aerodynamic constraints and terrain database) passes
through the perceptual and attention process of the SOM to the UWR (Gore, Corker, 2001).
In addition, a direct feed to the UWR from the simulated world representation is also
The UWR represents the agent’s working memory, domain knowledge and activity
structure of the tasks to be completed (Gore, Corker, 2001). An example of working memory
would be a short-term recollection of a previous work out put that is necessary to continue
ones job to a next level. In addition, domain knowledge assumes that the simulated human
has previous experience with the machine, and thus is applicable for a human-machine
system that has matured. This UWR passes information to a scheduler within the SOM that
Defining Characters Air MIDAS 6
determines the resources available for the completion of the activity. The environment
triggers the activities (procedure) within the agent and completes the desired procedure in
accordance with the availability of the resources in the agent. The scheduler invokes rules to
determine the triggering procedures (Gore, Corker, 2001). Consequently, postponed,
suspended, working, current or pending procedures fall in the workflow simulation. In turn,
the SOM selects activities to perform, some of which interact with the representation of
equipment in the simulated world and change the behavior of the relevant part of the system
(Gore, Corker, 2001).
After the appropriate scheduling and assigning of work takes place within the SOM,
the actual action execution by the motor activity representation of the software takes place.
This representation feeds forward necessary functions based on the resource available per the
instructions given by the scheduler. It also feeds back actual happening to the attention and
perception representation of the SOM and the planning representation of the same module.
By so doing, it simulates the work procedure and the entailed workload and time taken to do
multiple or single task(s) by an operator or a team of operators.
From the above description, it is evident that the Air MIDAS simulation software is a
closed loop object oriented software. In order for it to function, the software has to initiate an
iterative process that will recognize the working environment, the available resources and the
instantaneous work status. All these benchmarks, collectively termed as objects of
recognition, guide the software to come to a result. In addition, the fact that process flows
follow an iterative path from perception to scheduling to motor activities and back to the
perception representation as feedback, testify that the system is a closed loop one.
In his Master’s Thesis paper on the subject of distributed cognitive process, Brian F.
Gore (1999) describes the workflow of man-machine simulation on MIDAS as follows,
Defining Characters Air MIDAS 7
“The model designer that intends to learn about human-machine relations enters the
system through the Graphical User Interface (GUI) that provides the main interaction
between the designer and the system. The user selects among four functions in the
system. To start working on Air MIDAS requires the user to establish a domain
model. The user can then select the graphical animation or view to support one or
more simulations. The user is able to specify in the simulation module the execution
and display parameters for a given simulation set, and specify the data to-be-collected
and analyzed in the results analysis because of running the simulations. The results
analysis system also provides functionality for archival processes for various
simulation sessions. The other underlying MIDAS architectural components include a
human operator model, memory representation models, attentional control models,
activity representation models, task activity models, and decision-making models.
These seven models interact to produce human behavior that is based on
Gore (1999) continues the description on the workflow of the simulation process as
The domain model consists of a library of descriptors that support the creation of
vehicle characteristics, environment characteristics, crew station/equipment
characteristics, the human operator model (HO), mission and activity models,
memory representation, attention control, task agenda and decision making models.
HO allows for the production of behavior and responses for single and multiple
operators in the scenarios. The HO is composed of sub-models in an integrated format
including an anthropometric model, sensation and perception models, attention(and
other resource models), central processing cognitive functions such as decision-
making, evaluation and action selection, and finally behavioral models to guide the
Defining Characters Air MIDAS 8
anthropometric model in the execution of action. The second model that is vital for
MIDAS is the goal related model. This goal related model makes the MIDAS
architecture First Principled because this is where the goals are stored within the
Another trait of the Air MIDAS software is that it is event driven. The fact that Air MIDAS
feeds forward actions or procedures for execution and feeds back status of the proceedings of
the action makes it an event- driven rather than situation-driven simulation software (Pew,
The Air MIDAS software like any HOOTL tool runs in one of two ways when being
used to analyze a man machine relationship. The first one includes a process in which
variables that are pertinent to the man-machine relation established manipulate, on a
predefined scenario. This involves changing a specific variable at a predefined time in the
simulations. One simulation is termed as a single Monte Carlo simulation. Once these
variables change, a second series of Monte Carlo simulation runs take place. Consequently,
comparison of the results happens in order to verify and validate the credibility of the
modeling tool, which in this case is Air MIDAS (Gore, Corker, 1999).
A second procedure entails an experimental design, and has the human performance-
modeling tool generate responses given the manipulations outlined in the experimental
design. The modeling software selects from a common random number (CRN) table and uses
this human performance value in the Monte Carlo runs (Gore, Corker, 1999). These runs are
performed a number of times similar to testing a number of subjects in a HITL simulation and
deducing conclusions based on the output from the subjects. The data from CRN matrix that
the human performance-modeling tool uses in its generation of response values comes from
human performance and human performance cognition research (Gore, Corker, 1999). The
Defining Characters Air MIDAS 9
human performance modeling software selects from the CRN table built within each
modeling tool and then uses these values in the Monte Carlo simulation run.
Both the above procedures of running Air MIDAS have one goal. This is anticipating
the amount of time covered to do a single or a combination of tasks. Coherent with this
extraction of the human model workload occurs. As this software can anticipate both single
and multiple human operator workloads, it is possible to analyze crew workload
management. Thus, Air MIDAS can analyze induced changes in an existing crew operated
system or future crew operated systems with adequate real world representation in terms of
workload and usage time.
In this paper, Air MIDAS, which is a HOOTL simulation software has been
characterized based on its defining features. One feature that differentiates this particular
software from others that do the same job is the representation of the high-level behaviors
that are characteristic to humans. This feature makes the evaluation carried out by Air
MIDAS much more stringent. Consequently, gathering detailed results from such evaluations
However, since the representation of detailed human character is involved in the
software, it is necessary to have a database upon which the software bases such characters.
This is because lack of such information will compromise the simulation process of the
software and flaw the results. Information about human character like perception is gathered
in ways that include HITL simulations, work place observations etc. Thus, the optimal
operation of Air MIDAS is dependant on human character data.
Another observation made is on the path of the workflow simulated by the software.
A single accomplishment loop will involve perception, UWR, motor simulation, scheduler
and external world database. In order to run this loop of activities an efficient processor is
Defining Characters Air MIDAS 10
necessary that would guarantee results. Therefore, the use of Air MIDAS and its
advancement in the future ties with the future of data processing that is the availability of
powerful computers will enhance the capabilities of the Air MIDAS software.
Defining Characters Air MIDAS 11
Advisory Group for Aerospace Research and Development (AGARD) (1998). A designer’s guide to
human performance modeling (North Atlantic Treaty Organization AGARD Advisory Report
356). Canada Communication Group Inc.: Hull, Canada.
Foyle, D.C. and Hooey, B.L. (2008). Human Performance Modeling in Aviation. Boca Raton, FL.:
Gore, B.F. (1999). Modeling distributed cognition: System interaction in free flight. Master’s thesis:
UMI, Santa Monica, CA.
Gore, B.F. and Corker, K.M. (1999). System interaction in “free flight”: A modeling tool cross
comparison. Proceedings of the Digital Human Modeling Conference and Exposition,
Gore, B.F. and Corker, K.M. (2001). Human Performance Modeling: A Cooperative and Necessary
Methodology for Studying Occupational Safety. Advances in Occupational Ergonomics and
Safety. Amsterdam: IOS Press
Laughery, K.R. Jr., and Corker, K.R. (1997). Computer modeling and simulation of human/system
performance. Handbook of human factors and ergonomics (Second Edition.), N.Y.: Wiley
and Sons, Inc., (pp. 1375-1408).
Pew, R.W. and Mavor, A.S. (1998). Modeling Human and Organizational Behavior: Application to
Military Simulations. Washington D.C.: National Academy Press.
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