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<TITLE>Papers on Machine Learning</TITLE>
<H1> Papers on Machine Learning </H1>
<P><HR>
Jaime Carbonell,
Oren Etzioni,
Yolanda Gil,
Robert Joseph,
Craig Knoblock,
Steven Minton,
and Manuela Veloso.
"Planning and Learning in PRODIGY: Overview of an Integrated Architecture".
In <i>Goal-Driven Learning</i>, Aswin Ram and David Leake (Eds.),
MIT Press 1995.
<P>
<P><HR>
Yolanda Gil.
"Learning by Experimentation:
Incremental Refinement of Incomplete Planning Domains".
<i>Proceedings of the Eleventh International
Conference on Machine Learning</i>July 10-13, 1994, Rutgers, NJ.
(<A HREF="http://www.isi.edu/~gil/papers/gil-mlc94.ps">Postscript file </A>)
<P>
<B> Abstract: </B>
Building a knowledge base requires iterative refinement to
correct imperfections that keep lurking after each new version of the system.
This paper concentrates on the automatic refinement of
incomplete domain models for planning systems,
presenting both a methodology for addressing the problem
and empirical results.
Planning knowledge may be refined automatically
through direct interaction with the environment.
Missing conditions cause unreliable predictions of action outcomes.
Missing effects cause unreliable predictions of facts about the state.
We present a practical approach based on
continuous and selective interaction with the
environment that pinpoints the type of
fault in the domain knowledge that causes
any unexpected
behavior of the environment,
and resorts to experimentation
when additional information is needed to correct the fault.
Our approach has been implemented in EXPO, a system that uses PRODIGY
as a baseline planner and improves its domain knowledge in several domains
when initial domain knowledge is up to
50% incomplete.
The empirical results presented show that EXPO
dramatically improves its
prediction accuracy and reduces the amount of unreliable action outcomes.
<P>
<P><HR>
Yolanda Gil.
"Efficient Domain-Independent Experimentation".
<i>Proceedings of the Tenth International
Conference on Machine Learning</i>,
Amherst, MA, June 1993.
(<A HREF="http://www.isi.edu/~gil/papers/gil-mlc93.ps">Postscript file </A>)
<P>
<B> Abstract: </B>
Planning systems often make the assumption that omniscient
world knowledge is available.
Our approach makes the more realistic assumption that the
initial knowledge about the actions
is incomplete,
and uses experimentation as a learning mechanism when
the missing knowledge causes an execution failure.
Previous work on learning by experimentation
has not addressed the issue of how to choose good experiments,
and much research on learning from failure
has relied on background knowledge to build
explanations that pinpoint directly the causes of failures.
We want to investigate the potential
of a system
for efficient learning by experimentation without such background knowledge.
This paper describes domain-independent heuristics
that compare possible hypotheses and choose the ones most likely to
cause the failure.
These heuristics extract information solely from
the domain operators
initially available for planning
(incapable of producing such explanations)
and the planner's experiences in interacting with the environment.
Our approach has been implemented in EXPO, a system that uses PRODIGY
as a baseline planner and improves its domain knowledge in several
domains.
The empirical results presented show that EXPO's heuristics
dramatically reduce the number of experiments needed to
refine incomplete operators.
<P>
<P><HR>
Yolanda Gil.
"Learning New Planning Operators by Exploration and Experimentation".
<i>Proceedings of the AAAI Workshop on Learning Action
Models</i>, Washington, DC, July 1993.
(<A HREF="http://www.isi.edu/~gil/papers/gil-aaaiwks93.ps">Postscript file </A>)
<P>
<B> Abstract: </B>
This paper addresses a computational approach to the automated
acquisition of domain knowledge for planning systems via
experimentation with the environment. Our previous work has shown
how existing incomplete operators can be refined by adding missing
preconditions and effects. Here we develop additional methods to
acquire new operators such as direct analogy with existing operators,
decomposition of monolithic operators into meaningful sub-operators,
and experimentation with partially-specified operators.
<P>
<P><HR>
Yolanda Gil.
"Acquiring Domain Knowledge for Planning by Experimentation".
<i>Ph.D. Thesis</i>, School of Computer Science, Carnegie Mellon
University, Pittsburgh PA 15213. August 1992.
Available as CMU Technical Report CMU-CS-92-175.
<P>
<B> Abstract: </B>
In order for autonomous systems to interact with their environment in an
intelligent way, they must be given the ability to adapt and learn
incrementally and deliberately. It is virtually impossible to devise
and hand code all potentially relevant domain knowledge for complex
dynamic tasks. This thesis describes a framework to acquire domain
knowledge for planning by failure-driven experimentation with the
environment. The initial domain knowledge in the system is an
approximate model for planning in the environment, defining the
system's expectations. The framework exploits the characteristics
of planning domains in order to search the space of plausible
hypotheses without the need for additional background knowledge to
build causal explanations for expectation failures. Plans are
executed while the external environment is monitored, and
differences between the internal state and external observations
are detected by various methods each correlated with a typical
cause for the expectation failure. The methods also construct a
set of concrete hypotheses to repair the knowledge deficit.
After being heuristically filtered, each hypothesis is tested in turn
with an experiment. After the experiment is designed,
a plan is constructed to achieve the situation required to
carry out the experiment. The experiment plan must meet
constraints such as minimizing plan length and negative
interference with the main goals. The thesis describes a set of
domain-independent constraints for experiments and their
incorporation in the planning search space. After the execution
of the plan and the experiment, observations are collected to conclude if
the experiment was successful or not. Upon success, the hypothesis is
confirmed and the domain knowledge is adjusted. Upon failure, the
experimentation process is iterated on the remaining hypotheses
until success or until no more hypotheses are left to be considered.
This framework has shown to be an effective way to address incomplete
planning knowledge and is demonstrated in a system called EXPO,
implemented on the PRODIGY planning architecture.
The effectiveness and efficiency of EXPO's methods is
empirically demonstrated in several domains, including a large-scale
process planning task, where the planner can recover from situations
missing up to 50% of domain knowledge through repeated experimentation.
<P>
<P><HR>
Jaime Carbonell and Yolanda Gil.
"Learning by Experimentation:
The Operator Refinement Method".
<i>Machine Learning: An Artificial Intelligence
Approach, Volume III</i>, Michalski, R. S. and Kodratoff, Y. (Eds.),
Morgan Kaufmann, 1990.
(<A HREF="http://www.isi.edu/~gil/papers/gil-mlbook3.ps">Postscript file </A>)
<P>
<B> Abstract: </B>
Autonomous systems require the ability to plan effective courses of action
under potentially uncertain or unpredictable contingencies.
Planning requires knowledge of the environment that is accurate enough to
allow reasoning about actions.
If the environment
is too complex or very dynamic, goal-driven learning with
reactive feedback becomes a necessity. This chapter addresses the issue of
learning by experimentation as an integral component of PRODIGY.
PRODIGY is a flexible planning system that
encodes its domain knowledge as declarative operators, and applies
the operator refinement method to acquire additional preconditions
or postconditions when observed consequences
diverge from internal expectations. When multiple explanations for
the observed divergence are consistent with the existing domain knowledge,
experiments to discriminate among these explanations are generated.
The experimentation process isolates the deficient operator and inserts the
discriminant condition or unforeseen side-effect to
avoid similar impasses in future planning.
Thus, experimentation is demand-driven and exploits both the internal state
of the planner and any external feedback received. A detailed example of
integrated experiment formulation in presented as the basis for a
systematic approach to extending an incomplete domain theory or correcting
a potentially inaccurate
one.
<P>
<P><HR>
Steve Minton, Jaime G. Carbonell, Craig A. Knoblock,
Daniel R. Kuokka, Oren Etzioni, and Yolanda Gil.
"Explanation-Based Learning: A Problem-Solving Perspective".
<i>Artificial Intelligence</i>, 40(1-3):63-118,
September 1989.
<P>
<P><HR>
<A HREF="http://www.isi.edu/~gil/yg-homepage.html">
<i>Yolanda Gil</i></A>
<A HREF=mailto:[email protected]>[email protected]</A>