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Ted :: Early Research (Graduate School)
NOTICE: I AM LOOKING FOR A RESEARCH/TEACHING JOB! (tenuretrack assistant professor)
At the moment, I am seeking an engineering (electrical or computer science) or applied mathematics tenuretrack assistant professor position that
will let me continue scholarly research in distributed systems. I have expertise in biomimicry, optimal control,
hybrid systems, parallel algorithms, and agentbased modeling. My application areas span intelligent lighting,
robotics, and autonomous vehicles. I also have extensive teaching expertise in electrical engineering, including circuits, signals and systems, and control theory courses.



Research Overview, Observations, and Goals






 Short overview of completed research and research goals
 "Engineering Serendipity: Successes in Solitary Foraging and New Investigations in Cooperative TaskProcessing Networks" (LARGE PDF FILE: 4.5 MB) – Slides from a research seminar surveying biomimicry results and observations about biomimicry in general
 Abstract:
Biomimicry has been a great source of novel ideas for bad
technological design. Both engineers and biologists look for
concrete examples where behavioral models can be applied directly.
Consequently, engineers overlook important theoretical assumptions
behind those models, and biologists overlook nontraditional design
problems because of the attraction to applications with robots, the
charismatic megafauna of technology. Rather than using concrete
applications to drive interdisciplinary research, abstract
mathematical models can be generated to fit a range of disciplines.
Valuable insights into both design and analysis then come from
studying the assumptions necessary to specialize these models.
Additionally, advances in one field can better translate to another
when there are frameworks and communication shared between
disciplines.
First in this talk, the consequences of generalizing a simple model
of solitary foraging from behavioral ecology is discussed. In
particular, the process produces decisionmaking algorithms for
autonomous search as well as new explanations for both impulsiveness
and overstaying observed in animals. The talk then introduces a
generic model of asynchronous distributed cooperative task
processing that is meant to mimic human, nonhuman, and artificial
systems. Each agent is interested in maximizing its own utility;
however, patterns qualitatively similar to load balancing occur.
Although the model has little resemblance to the birthdeath
processes typically described in network cooperation theory, a
version of the networked generalization of Hamilton's rule emerges.
 LaTeX source code (Mercurial repository; powerdotbased presentation)






Coperative Task Processing






Doctoral Candidacy Exam
 PhD research proposal: "Cooperative Task Processing" (PDF) (source code repository)
 Written candidacy exam submission: "Research Problems in Distributed Control for Energy Systems" (PDF) (source code repository)
 Internal research group presentation (given later in January of 2010) of research results: "Cooperative Task Processing: A Framework" (PDF) (source code repository – Powerdot source code)






Design and Analysis of Optimal TaskProcessing Agents






Doctoral Dissertation
 Dissertation: Design and Analysis of Optimal TaskProcessing Agents (PDF [LARGE 2 MB]) (OhioLink) (source code repository)
 Abstract:
This dissertation is given in two parts followed by concluding remarks.
The first three chapters describe the generalization of optimal foraging
theory for the design of solitary taskprocessing agents. The following
two chapters address the coordinated action of distributed independent
agents to achieve a desirable global result. The short concluding
part summarizes contributions and future research directions.
Optimal foraging theory (OFT) uses ecological models of energy intake to
predict behaviors favored by natural selection. Using models of the
longterm rate of energetic gain of a solitary forager encountering a
variety of food opportunities at a regular rate, it predicts
characteristics of optimal solutions that should be expressed in nature.
Several engineered agents can be modeled similarly. For example, an
autonomous air vehicle (AAV) that flies over a region encounters targets
randomly just as an animal will encounter food as it travels. OFT
describes the preferences that the animal is likely to have due to
natural selection. Thus, OFT applied to mobile vehicles describes the
preferences of successful vehicle designs.
Although OFT has had success in existing engineering applications, rate
maximization is not a good fit for many applications that are otherwise
analogous to foraging. Thus, in the first part of this dissertation, the
classical OFT methods are rediscovered for generic optimization
objectives. It is shown that algorithms that are computationally
equivalent to those inspired by classical OFT can perform better in
realistic scenarios because they are based on more feasible optimization
objectives. It is then shown how the design of foraginglike algorithms
provides new insight into behaviors observed and expected in animals.
The generalization of the classical methods extracts fundamental
properties that may have been overlooked in the biological case.
Consequently, observed behaviors that have been previously been called
irrational are shown to follow from the extension of the classical
methods.
The second part of the dissertation describes individual agent behaviors
that collectively result in the achievement of a global optimum when the
distributed agents operate in parallel. In the first chapter,
collections of agents that are each similar to the agents from the early
chapters are considered. These agents have overlapping capabilities, and
so one agent can share the task processing burden of another. For
example, an AAV patrolling one area can request the help of other
vehicles patrolling other areas that have a sparser distribution of
targets. We present a method of volunteering to answer the request of
neighboring agents such that sensitivity to the relative loading across
the network emerges. In particular, agents that are relatively more
loaded answer fewer taskprocessing requests and receive more answers to
their own requests. The second chapter describes a distributed numerical
optimization method for optimization under inseparable constraints.
Inseparable constraints typically require some direct coordination
between distributed solver agents. However, we show how certain
implementations allow for stigmergy, and so far less coordination is
needed among the agents. For example, intelligent lighting, which
maintains illumination constraints while minimizing power usage, is one
application where the distributed algorithm can be applied directly.
 Defense presentation (powerdot slides): "Engineering Serendipity: Design and Analysis of Optimal TaskProcessing Agents" (PDF [LARGE 6 MB]) (source code repository)
Abstract:
Biomimicry has been a great source of novel ideas for bad
technological design. Both engineers and biologists look for
concrete examples where behavioral models can be applied directly.
Consequently, engineers overlook important theoretical assumptions
behind those models, and biologists overlook nontraditional design
problems because of the attraction to the charismatic megafauna of
technology (e.g., robots). In this talk, efforts to create general
optimal agentbased frameworks that can be specialized for different
disciplines are described.
First, a solitary taskprocessing agent framework is presented that
unifies behavioral models from engineering design and biological
analysis. Using this framework, an example decisionmaking algorithm
for autonomous vehicles is generated that performs better than the
conventional bioinspired algorithm. Because both algorithms fit
within the unified framework, they are computationally similar, and
autonomous vehicles using the bioinspired algorithm can be easily
modified to use the better performing algorithm. Furthermore,
ostensibly irrational observed animal behaviors that vary from the
predictions of classical optimal foraging theory are shown to be
optimal within the generalized taskprocessing framework.
Finally, the talk presents two frameworks for collective task
processing in groups of agents. First, a generic model of
asynchronous distributed cooperative task processing meant to mimic
human, nonhuman, and artificial systems is presented. Despite local
utility maximization at each agent, patterns emerge from the group
that are qualitatively similar to load balancing. Second, a
distributed solver for constrained optimization is presented that
has applications in power generation, intelligentlight design, and
eusocialinsectforaging analysis.




