Douglas Pearson (Douglas John Pearson)
Contact Information
Mail:
4649 Eastern Ave. N.
Seattle, WA 98103
Phone: (206) 675-1518
E-mail:
I was a graduate student in the Artificial
Intelligence Laboratory at the University
of Michigan. My research was on learning within intelligent agents
(specifically learning procedural planning knowledge if you know about
these things--you can even read a quick summary)
and I'm part of the SOAR
group. For my reference, these are my current bookmarks.
On-line Publications
My Thesis
Learning
Procedural Planning Knowledge in Complex Environments.
Ph.D. Thesis, 1996.
This is about how to learn to do tasks in difficult environments.
The main PDF is a version of my thesis generated in 2007 and is much easier to read than the original (from 1996) which is in the format of the actual printed thesis in case anyone wants it or wants to reference it.
If a thesis seems like a bit much to take in, try this for a shorter paper on the same subject.
Other Papers
-
Correcting
Imperfect Domain Theories: A Knowledge-Level Analysis.
in Machine Learning: Induction, Analogy and Discovery, edited by Susan
Chipman and Alan Meyrowitz, Kluwer Academic Press, 1992.
This is about the types of errors you can have in an agent's knowledge
of the world.
-
A
symbolic solution to intelligent real-time control.
in Robotics and
Autonomous Systems 11 (1993) (or try Elsevier
Publishers).
This is about designing a rule-based system to fly a simulated plane
in real-time.
-
Combining
learning from instruction with recovery from incorrect knowledge.
in Machine
Learning 95 Workshop on "Agents that learn from other agents" (1995)
This is about joining together two learning systems, one that corrects
mistakes during problem solving and the other that learns from English
instructions.
-
Active
Learning in Correcting Domain Theories: Help or Hindrance?
in AAAI
Symposium on Active Learning (1995)
This is about the relative merits of learning by doing, rather than
learning by being given carefully chosen sample problems.
-
Toward
Incremental Knowledge Correction for Agents in Complex Environments.
to appear in Machine
Intelligence 15 (1999)
This is a general overview of my system that learns to correct mistakes
as it solves problems. The system is called IMPROV.
-
Learning
Procedural Planning Knowledge in Complex Environments
to appear in AAAI-96
Student Abstract Program (1996).
This is a one page summary of my thesis work.
-
Knowledge-directed
Adaptation in Multi-Level Agents.
in AAAI-96 Workshop
on Intelligent Adaptive Agents (1996).
This is about learning at different time-scales within a single system.
-
Dynamic Knowledge Integration during Plan Execution.
in AAAI-96
Fall Symposium on Plan Execution: Problems and Issues (1996).
This is about how tasks and environments contrain the way plans can
be built and used.
Keywords: Machine Learning, Intelligent Agents, Error Recovery, Theory
Revision, Instruction, Induction, IMPROV, Soar, Production Rule Systems,
Procedural Knowledge
-
Example-driven Diagrammatic Tools For Rapid Knowledge Acquisition.
in Visualizing Information in Knowledge Engineering (2003)
This is about a tool we're building that lets users construct intelligent systems through building a series of diagrams
Keywords: Knowledge acqusition, behavior acquisition, diagrams, visualization, example-driven,
human behavior models, rule based.
-
Redux: Example-Driven Diagrammatic Tools for Rapid Knowledge Acquisition.
in Behavior Representation in Modeling and Simulation (2004)
This is more about our tool, Redux, that lets users construct intelligent systems through building a series of diagrams
Keywords: Knowledge acqusition, behavior acquisition, diagrams, visualization, example-driven,
human behavior models, rule based.
-
Learning through Interactive Behavior Specifications
in AAAI Symposium on Mixed-Initiative Problem Solving (2005)
This is about combining Redux (our rapid graphical knowledge acquisition tool) with a learning-by-observation machine learning tool.
Keywords: Knowledge acqusition, behavior acquisition, diagrams, visualization, example-driven,
human behavior models, learning by observation, machine learning, rule based.
-
Incremental Learning of Procedural Planning Knowledge in Challenging Environments
In Computational Intelligence on "Learning to Improve Reasoning" (November 2005)
-
Storm: A Framework for Biologically-Inspired Cognitive Architecture Research
In 8th International Conference on Cognitive Modeling, July 2007, Ann Arbor, Michigan
Soar Research Information
As part of my research, I've chopped out a piece of my system and I'm making
it available here. Follow this link to find out more about induction using
the
new
version of SCA.
To see an example of data-chunking using a method proposed by Rick Lewis
a few workshops back, click here
for a tar file. Read the READ.ME
file for more information.
Tag-o-Rama
We recently had the first Tag-o-Rama: a bunch of intelligent "taggles"
running round a maze trying to fry each other with flashlights. Lots of
good geeky fun, especially as I took first place :
Home Information
Recently moved to Seattle:
Mail:
Douglas J. Pearson
4649 Eastern Ave. N.
Seattle, WA 98103
Phone: (206) 675-1518
After hours, I enjoy a wide range of sports, especially
badminton
which isn't too popular here in the U.S.
Born and bred in Oxford, England...
 |
Did my Bachelor's degree at the
University
of St. Andrews in Scotland.
That's the (little) second inlet on the right going up the east coast
(the first, big one's the Firth of Forth--that's where Edinburgh is). |
Try out a Java Script to compute today's date and
time. Or try out a CGI script to read
a form. Or another to get your environment
variables. Or some straight Java.
Check on usage counters.
Douglas
Pearson
)
[2007]