papers-yg.html

Yolanda Gil.
"A Specification of Manufacturing Processes for Planning".
<i>Technical Report CMU-CS-91-179</i>, School of Computer Science, 
Carnegie Mellon University, Pittsburgh PA 15213.
(<A HREF="gil-processp91.ps">Postscript file </A>) 
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Abstract: 
Much research is being done on the 
automation of manufacturing processes.
The planning component in the production stage is very significant, 
due to the variety of alternative processes, their complexity, and 
their interactions.
This document describes a specification of some manufacturing processes,
including the machining, 
joining, and finishing of parts. 
The aim of this specification is not to be comprehensive or detailed, but to
present the AI community with a model of a complex and realistic 
application, and to use it to demonstrate
the feasibility of effective implementations of 
large-scale complex domains in a general-purpose architecture.
This specification has been successfully demonstrated in the PRODIGY 
architecture, and is one of the largest domains 
available for general-purpose planners.<P>

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Yolanda Gil.
"Integrated Architectures for Artificial Intelligence".
<i>Proceedings of the International Congress on New Horizons of 
Articicial Intelligence: Nonconventional Computing: Towards
Intelligent 
Systems</i> (invited paper). Mexico City, Mexico. April, 1991.
(<A HREF="gil-teccomp91.ps">Postscript file </A>) 
<P>

Abstract: 
Artificial Intelligence researchers have produced models of different
components required for 
intelligence such as knowledge representation, reasoning, 
natural language processing, learning,
neural computation, and robotics.
While isolating these components is vital for studying the 
important issues in each area, nobody believes that
these subfields have been completely solved.
The field has produced many systems that exhibit 
interesting behavior or intelligent results,
yet we know very little about how to combine their power.
In recent years, there has been an increasing interest in 
the integration of different aspects of intelligence in 
systems known as integrated architectures and that are
capable of producing general intelligent behavior.
The design space of integrated architectures is large and not well 
understood. 
Researchers are exploring the different issues by exploring points
in that space, trying to gain a better understanding in the process.
This paper discusses what constitutes
an integrated architecture, and what
the desired capabilities of such a system are.
Next, we introduce some of the architectures that have been proposed.
We conclude with a discussion that compares the 
architectures and their stands on different design issues
and a 
reflection on what is still missing in
the current systems.
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Yolanda Gil.
"Efficient Domain-Independent Experimentation".
<i>Proceedings of the Tenth International
Conference on Machine Learning</i>,
Amherst, MA, June 1993.
(<A HREF="gil-mlc93.ps">Postscript file </A>) 
<P>

Abstract: 
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.
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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>

Abstract: 
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.
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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="gil-mlbook3.ps">Postscript file </A>) 
<P>

Abstract: 
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.
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