INTELLLIGENT SYSTEMS ROADMAP
Topic Area: Computational Intelligence (CI)
David Casbeer, AFRLV, WPAFB AFB, OH
Nicholas Ernest, Psibernetix InC, Liberty Township, OH
Kelly Cohen, University of Cincinnati, Cincinnati, OH
“If we knew what it was we were doing, it would not be called research, would it?”
Albert Einstein
Introduction
This contribution to the Roadmap for Intelligent Systems will focus on Computational
Intelligence. “Computational intelligence is the study of the design of intelligent
agents,” where an agent is an entity that reacts and interacts with its environment
(David Poole, 1998). An intelligent agent refers to an agent that adapts to its
environment by changing its strategies and actions to meet its shifting goals and
objectives. “Just as the goal of aerodynamics isn’t to synthesize birds, but to
understand the phenomenon of flying by building flying machines, CI’s ultimate goal
isn’t necessarily the full-scale simulation of human intelligence.” As [aerospace]
engineers we seek to utilize the science of intelligence as learned through the study
of CI, not for “psychological validity but with the more practical desire to create
programs that solve real problems” [1].
Computational Intelligence is a non-traditional aerospace science, yet it has been
found useful in numerous aerospace applications, such as aerospace and remote
sensing (Lary, 2010), scheduled plans for unmanned aerial vehicles, improve
aerodynamic design (e.g. airfoil and vehicle shape), optimize structures, improve the
control of aerospace vehicles, regulate air traffic, etc. [3]. Traditional aerospace
sciences such as propulsion, fluid dynamics, thermodynamics, stability and control,
structures, and aeroelasticity utilize first principles or statistical models to
understand the system in question, and then use mathematical or computational
tools to construct the desired outcome. Naturally, to build these complex systems, a
deep understanding of the underlying physics is required. Years of research by
many people were needed to develop this theoretical foundation, upon which these
systems could be built.
Computational Intelligence Capabilities and Roles
Description of Computational Intelligence Capabilities
Computational Intelligence methods, including evolutionary computing, fuzzy logic,
bio-inspired computing, artificial neural networks, swarm intelligence and others,
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have demonstrated the potential in providing effective solutions to large scale,
meaningful and increasingly complex aerospace problems involving learning,
adaptation, decision making and optimization.
As the complexity and uncertainty in future aerospace applications increase, the
need to make effective as well as real time (or near real time) decisions while
exploring very large solution spaces is quintessential. The salient figures of merit in
the above class of applications is the quality of the decision made, which is based on
the minimization of a cost function, and computational cost while adhering to very
large number of system level and sub-system level constraints which include safety
and security of operations.
The outcome of this effort in seeking computationally intelligent tools are … ?
Computational Intelligence Roles and Example Applications
There are problems in aerospace that we cannot solve today using these typical
approaches, e.g., aircraft with uncertain models (hypersonics), missions where
objectives are given in linguistic/fuzzy terms, planning robustly for highdimensional complex/nonlinear systems with uncertainty. An example of a largescale problem of growing importance is the integration of unmanned aerial vehicles
within the national airspace. Moreover, as we consider autonomous Unmanned
Combat Aerial Vehicle (UCAV) in the 2030 time-frame, it becomes apparent that the
mission, flight, and ground controls will utilize the emerging paradigm of
Computational Intelligence; namely, the ability to learn, adapt, exhibit robustness in
uncertain situations, make sense of the data collected in real- time and extrapolate
when faced with scenarios significantly different from those used in training [4].
Equipped with advanced sensors, a limited supply of Self-Defense Missiles (SDMs),
and a recharging Laser Weapon System (LWS), these UCAVs can navigate a mission
space, counter enemy threats, cope with losses in communications, and destroy
mission-critical targets. Monte Carlo simulations of the resulting controllers were
tested in mission scenarios that are distinct from the training scenarios to
determine the training effectiveness in new environments and the presence of deep
learning. Despite an incredibly large solution space, a cascading genetic fuzzy tree
approach has demonstrated remarkable effectiveness in training intelligent
controllers for the UCAV squadron and shown robustness to drastically changing
states, uncertainty, and limited information while maintaining extreme levels of
computational efficiency [5].
The motivation for using CI in complex Aerospace Engineering problems stems from
two main reasons that pertain to highly complex problems. Primarily, there is a fair
amount of uncertainty in developing accurate models for simulation purposes.
Analytical approaches are generally limited to small-scale problems and further
research in utilizing fundamental principles is desirable but may be illusive.
Secondly, the problem might be intractable given today’s computational tools and
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hardware. We propose that the knowledge gained from the field of computational
intelligence can find practical solutions to some of these problems, and that in the
future it will become increasingly useful for aerospace systems.
Technical Challenges and Technology Barriers
Technical Challenges
Traditionalists see the work being done by aerospace computational intelligence
researchers and reject this approach because the foundation does not appear sound.
Their argument is valid: how can we solve something without really understanding
the problem? To address this challenge, a tight integration of computational
intelligence theory, which is based in computer science, with traditional aerospace
sciences is the only way forward. The application of CI to aerospace problems must
only happen when there is a true understanding of the problem and when CI offers
tools that have the potential to overcome the limitations of traditional solutions.
Furthermore, certain CI tools are more amenable to incorporating subject matter
experts; it is these tools that allow workable insight that will prove more useful,
because it incorporates experience and knowledge. Certain AFRL applications
concerning collaborating UAVs require that a successful system must show deep
learning, be computationally efficient, resilient to changes and unknown environments,
and ultimately be highly effective [6].
Technical Barriers
Closely related to the previous argument is the barrier that many CI tools come in
the form of a “black box.” The output and learning offers little intuition to the user of
such tools. In this regard, it is necessary to fully understand the problem, before
applying “black box” tools. Doing so will help alleviate some of this concern.
However, there is a need to develop CI tools and understanding that allow us to gain
an intuition into the result. Similarly, applying the appropriate tool to the specific
problem is important. Knowledge of the aerospace problem is required, as well as
an understanding of the CI tools. This gives the researcher the best ability to
practically solve the problem. More importantly, in order to develop the necessary
level of trust that the end-users of intelligent aerospace systems have in the results
of the CI tools, the CI tools will have to demonstrate a level of transparency that
sheds light into that “black box” and allows the users to understand why a certain
decision has been made by the CI tools. Some CI tools, like fuzzy logic, are more
transparent, and some, e.g., neural networks, will require additional work.
A major concern is that many CI tools do not offer analytic performance guarantees.
Evolutionary based methods cannot indicate how close they are to optimal.
Learning methods do not provide bounds to indicate how far the current solution is
from the truth. Currently, these guarantees are given through extensive testing and
evolution of a prototype system. This method is not out of the norm. Typical
airplanes today must pass certain testing thresholds to validate their performance.
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However, we can and should do better. Thought must be devoted to the
development of methodology to validate and verify performance bounds in more a
rigorous manner.
Impact to Aerospace Domains and Intelligent Systems Vision
The ability to bring high-performance and efficient control to difficult problems with
a far less intimate study of the physics behind the system, and thus fewer, if any,
unrealistic mathematical assumptions and constraints is the highlight of the CI tools.
This may lead to the counter-intuitive opportunity for CI methods to help firstprinciples approaches more quickly increase their accuracy.
This all paints a picture of CIs that seems too good to be true; as long as the inputs
and desired outputs are known, any problem could be solved by CI with no other
knowledge. The devil is in the details however, as CIs generally suffer heavily from
the “curse of dimensionality”. Recently, it has been shown that certain CI hybrids
such as Genetic fuzzy systems to develop “virtual subject matter experts” utilizing
insights gained during simulation based training to produce a set of linguistic rules
that are transparent and deterministic [4].
Following the effort in [4], we have demonstrated that CI can provide real-time
decision making for an uncertain, constrained optimization problem where the
solution space is huge.. This ability opens up the imagination and enables us to
boldly envision a wide variety of future aerospace applications involving numerous
interactions between teams of humans and increasingly autonomous systems. An
additional advantage of this class of the hybrid CI approaches is that while the
exploration of the solution space utilizes stochastic parameters during the learning
process, once the learning system converges to a solution, the subsequent decision
making is deterministic which lends itself far better for validation and verification.
Research Needs to Overcome Technology Barriers
Research Gaps
The ability and potential of CI to efficiently explore large solution spaces and provide
real-time decision-making for an AFRL related scenario of collaborating UCAVs has
been demonstrated [4]. The generality of CI techniques for a wider range of applications
needs to be explored and comparison made with alternative approaches for large-scale
complex problems. We feel that potential users need to see more evidence of the
applicability and suitability of CI and the role it may play in systems they have in mind.
Furthermore, research is much needed in developing verification and validation
techniques that will set the stage of implementing CI based solutions and incorporating
them in the full scale development program of future aerospace systems. This leads to
another research gap that is during the specification stages early on in a large complex
project. How can developmental risks associated with performance, robustness,
adaptability and scalability be assessed early on? What is the nature of the tasks required
during the conceptual design phase as we compare alterative approaches?
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Operational Gaps
Traditional aerospace Use Cases tend to limit themselves when it comes to concerns
about autonomous decision making in uncertain large-scale problems. Operational
doctrine development and technology advancement need to go hand-in-hand as they are
far more coupled in an increasingly complex aerospace environment. A simulation-based
spiral effort may be required to enhance the “daring” and to develop the confidence in the
development of operational doctrines. This calls for interaction between two communities
that traditionally do not exchange much in terms of early research and exploration of
ideas and exploitation of potentially powerful computational intelligent tools.
Research Needs and Technical Approaches
The following describe the desired features we seek using CI approaches:
 Develop Use Cases that involve large-scale complex aerospace applications with
inherent uncertainty and incomplete information.
 Develop a simulation-based environment to explore the Use Cases and establish
figures of merit for specifying tasks to be performed by CI agents.
 Explore the potential of different CI approaches and hybrids in the above-mentioned
simulated environment.
 Quantitatively evaluate the effectiveness of the developed CI approaches and hybrids
examining strengths, weaknesses and application areas they most lend themselves to.
 Develop V&V techniques, which will establish trust in CI approaches across the
aerospace community.
 Develop a CI repository of Use Cases, approaches, results and recommendations to
be shared by the community
 Implement the ability of integrating CI with hardware/software architectures to
enhance the intelligence of future aerospace applications.
A key to success will be the ability to imagine technologically achievable (from hardware
perspective) future Use Cases and quantify the impact of verifiable CI approaches.
Prioritization
Like with several other areas in the filed of Intelligent Systems, our first priority and the
main impediment is not technical, but rather insufficient funding levels. We have
demonstrated evidence of effective CI tools and this needs to be further explored and then
exploited to the fullest in making future aerospace applications that are much more
intelligent.
Secondly, we need more involvement from DOD and non-DOD funding agencies, which
develop appropriate challenge problems which can engage our community and allow for
a more open discussion and comparison of CI approaches and their ability to be
implemented in meaningful aerospace applications.
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Bibliography
[1] David Poole, A. M. (1998). Computational Intelligence: A Logical Approach. Oxford
University Press.
[2] Lary, D. J. (2010). Artificial Intelligence in Aerospace. In T. T. Arif (Ed.), Aerospace
Technologies Advancements. InTech.
[3] Task Force, Emerging Technologies Technical Committee, IEEE Computational
Intelligence Society, Computational Intelligence in Aerospace Applications, URL
below obtained on March 6, 2015
http://www.stardust2013.eu/Research/IEEEComputationalIntelligenceinAerospaceScien/tabid/4513/Default.aspx
[4] Ernest, N., " Genetic Fuzzy Trees for Intelligent Control of Unmanned Combat
Aerial Vehicles", PhD Dissertation, Department of Aerospace Engineering and
Engineering Mechanics, University of Cincinnati, April 2015.
[5] N. Ernest, K. Cohen, C. Schumacher, and D. Casbeer. Learning of intelligent
controllers for autonomous unmanned combat aerial vehicles by genetic
cascading fuzzy methods. SAE Aerospace Systems Technology Conference,
Cincinnati, OH, 2014.
[6] N. Ernest, K. Cohen, E. Garcia, C. Schumacher, and D. Casbeer. Multi- agent
Cooperative Decision Making using Genetic Cascading Fuzzy Systems. AIAA
SciTech Conference, Kissimmee, FL., 2015.
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