CMSC 491M/691M - Spring 2003
Discussion Questions for Class #6, February 12
Reading: Veloso et al.
PRODIGY
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What planning method does PRODIGY use? What are the "head-plan" and the
"tail-plan?" Why does PRODIGY bother with the "head-plan" at all?
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The focus of PRODIGY is on learning in planning. What are the learning
methods in PRODIGY?
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What does it mean for a search-control decision to be a "learning opportunity?"
What do the PRODIGY designers mean by having a "glass-box" planner?
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EBL, STATIC, and the other algorithms in section 3.1 just restate existing
knowledge. In that case, how can they be said to be "learning?" When is
this type of learning valuable? What are the tradeoffs in this type of
learning?
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Does the QUALITY learning algorithm seem more like "learning to improve
efficiency (section 3.1) or "learning domain knowledge" (section 3.2)?
What are the limitations of this approach to learning about plan quality?
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If you were going to extend PRODIGY for your master's thesis, what aspect
would you focus on?
PRODIGY vs. Soar/ACT-R
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Is Soar's chunking more like the learning methods in 3.1 (learning to improve
efficiency) or more like those in 3.2 (learning domain knowledge)?
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One striking difference between PRODIGY and ACT-R/Soar is that the latter
are very uniform, general architectures, whereas PRODIGY feels more like
a framework for a "bag of tricks" (collection of various learning
algorithms). What are the advantages and disadvantages of each of these
architectural approaches?
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What are the key differences (other than the above) between PRODIGY and
ACT-R/Soar? Try to identify domains that would be good for the former but
not the latter, and vice versa. What about domains that would not
be particularly good for either PRODIGY or ACT-R/Soar?