http:^^www.cs.utexas.edu^users^ml^speedup.html

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<title>Speedup Learning</title>

<h1>Speedup Learning/Learning for Planning</h1>

To view a paper, click on the open book image. <br> 
<br>


<ol>


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<a name="scope-msl-96.ps.Z"</a>

<b><li> Integrating EBL and ILP to Acquire Control Rules for Planning<br></b>
Tara A. Estlin and Raymond J. Mooney<br>

<cite>Proceedings of the Third International Workshop on
Multi-Strategy Learning</cite>, pp. 271-279, Harpers Ferry, WV, May
1996. (MSL-96).<p>

<blockquote>
Most approaches to learning control information in planning systems
use explanation-based learning to generate control rules.
Unfortunately, EBL alone often produces overly complex rules that
actually decrease planning efficiency.  This paper presents a novel
learning approach for control knowledge acquisition that integrates
explanation-based learning with techniques from inductive logic
programming. EBL is used to constrain an inductive search for
selection heuristics that help a planner choose between competing plan
refinements. SCOPE is one of the few systems to address learning
control information in the newer partial-order planners.
Specifically, SCOPE learns domain-specific control rules for a version
of the UCPOP planning algorithm. The resulting system is shown to
produce significant speedup in two different planning domains.
</blockquote>

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<a name="scope-aaai-96.ps.Z"</a>

<b><li>Multi-Strategy Learning of Search Control for Partial-Order
Planning<br></b>
Tara A. Estlin and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 843-848, Portland, OR, August, 1996. (AAAI-96)<p>


<blockquote>
Most research in planning and learning has involved linear,
state-based planners. This paper presents SCOPE, a system for learning
search-control rules that improve the performance of a partial-order
planner.  SCOPE integrates explanation-based and inductive learning
techniques to acquire control rules for a partial-order planner.
Learned rules are in the form of selection heuristics that help the
planner choose between competing plan refinements.  Specifically,
SCOPE learns domain-specific control rules for a version of the UCPOP
planning algorithm. The resulting system is shown to produce
significant speedup in two different planning domains.
</blockquote>

<!WA2><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/scope-aaai-96.ps.Z">
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<a name="scope-proposal-96.ps.Z"</a>

<b><li> Integrating Explanation-Based and Inductive Learning Techniques
to Acquire Search-Control for Planning<br></b>
Tara A. Estlin<br>

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1996. <p>

<blockquote>
Planning systems have become an important tool for automating
a wide variety of tasks. Control knowledge guides a planner to find
solutions quickly and is crucial for efficient planning in most
domains.  Machine learning techniques enable a planning system to 
automatically acquire domain-specific search-control knowledge for
different applications.  Past approaches to learning control
information have usually employed explanation-based learning
(EBL) to generate control rules.  Unfortunately, EBL alone often
produces overly complex rules that actually decrease rather than
improve overall planning efficiency.  This paper presents a novel
learning approach for control knowledge acquisition that integrates
explanation-based learning with techniques from inductive logic
programming. In our learning system SCOPE, EBL is used to constrain
an inductive search for control heuristics that help a planner choose
between competing plan refinements. SCOPE is one of the few systems
to address learning control information for newer, partial-order
planners.  Specifically, this proposal describes how SCOPE learns
domain-specific control rules for the UCPOP planning algorithm. The
resulting system is shown to produce significant speedup in two
different planning domains, and to be more effective than a pure EBL
approach.  <p>

Future research will be performed in three main areas.  First, SCOPE's
learning algorithm will be extended to include additional techniques
such as constructive induction and rule utility analysis.  Second,
SCOPE will be more thoroughly tested; several real-world planning
domains have been identified as possible testbeds, and more in-depth
comparisons will be drawn between SCOPE and other competing
approaches.  Third, SCOPE will be implemented in a different planning
system in order to test its portability to other planning algorithms.
This work should demonstrate that machine-learning techniques can be a
powerful tool in the quest for tractable real-world planning.<p>
</blockquote>

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<a name="dolphin-ewsp-95.ps.Z" </a>

<b> <li> Hybrid Learning of Search Control for Partial-Order Planning <br> </b>

Tara A. Estlin and Raymond J. Mooney <br>

<cite>New Directions in AI Planning</cite>, M. Ghallab and A. Milani, Eds,
IOS Press, 1996, pp. 129-140. <p>  

<blockquote>
This paper presents results on applying a version of the DOLPHIN
search-control learning system to speed up a partial-order planner.
DOLPHIN integrates explanation-based and inductive learning techniques
to acquire effective clause-selection rules for Prolog programs.  A
version of the UCPOP partial-order planning algorithm has been
implemented as a Prolog program and DOLPHIN used to automatically
learn domain-specific search control rules that help eliminate
backtracking. The resulting system is shown to produce significant
speedup in several planning domains.
</blockquote>

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<a name="ilp-ebl-sigart-94.ps.Z" </a>

<b> <li> Integrating ILP and EBL </b> <br>  

Raymond J. Mooney and John M. Zelle <br> 

<cite> SIGART Bulletin</cite>, Volume 5, number 1, Jan. 1994, pp 12-21. <p>

<blockquote>
This paper presents a review of recent work that integrates methods from
Inductive Logic Programming (ILP) and Explanation-Based Learning (EBL).  ILP
and EBL methods have complementary strengths and weaknesses and a number of
recent projects have effectively combined them into systems with better
performance than either of the individual approaches. In particular, integrated
systems have been developed for guiding induction with prior knowledge
(ML-SMART, FOCL, GRENDEL) refining imperfect domain theories
(FORTE, AUDREY, Rx), and learning effective search-control
knowledge (AxA-EBL, DOLPHIN).
</blockquote>

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<a name="dolphin-chill-proposal-93.ps.Z" </a>

<b> <li> Learning Search-Control Heuristics for Logic Programs:
Applications to Speedup Learning and Language Acquisition </b> <br>

John M. Zelle <br> 

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1993. <p>

<blockquote>
This paper presents a general framework, learning search-control heuristics for
logic programs, which can be used to improve both the efficiency and accuracy
of knowledge-based systems expressed as definite-clause logic programs.  The
approach combines techniques of explanation-based learning and recent advances
in inductive logic programming to learn clause-selection heuristics that guide
program execution.  Two specific applications of this framework are detailed:
dynamic optimization of Prolog programs (improving efficiency) and natural
language acquisition (improving accuracy).  In the area of program
optimization, a prototype system, DOLPHIN, is able to transform some
intractable specifications into polynomial-time algorithms, and outperforms
competing approaches in several benchmark speedup domains.  A prototype
language acquisition system, CHILL, is also described.  It is capable of
automatically acquiring semantic grammars, which uniformly incorprate syntactic
and semantic constraints to parse sentences into case-role representations.
Initial experiments show that this approach is able to construct accurate
parsers which generalize well to novel sentences and significantly outperform
previous approaches to learning case-role mapping based on connectionist
techniques.  Planned extensions of the general framework and the specific
applications as well as plans for further evaluation are also discussed.
</blockquote>

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<a name="dolphin-ijcai-93.ps.Z" </a>

<b> <li> Combining FOIL and EBG to Speed-Up Logic Programs </b> <br> 

John M. Zelle and Raymond J. Mooney <br> 

<cite> Proceedings of the Thirteenth International Joint Conference on Artificial
Intelligence</cite>, pp. 1106-111, Chambery, France, 1993. (IJCAI-93) <p>

<blockquote>
This paper presents an algorithm that combines traditional EBL
techniques and recent developments in inductive logic programming to
learn effective clause selection rules for Prolog programs.  When
these control rules are incorporated into the original program,
significant speed-up may be achieved.  The algorithm is shown to be an
improvement over competing EBL approaches in several domains.
Additionally, the algorithm is capable of automatically transforming
some intractable algorithms into ones that run in polynomial time.
</blockquote>

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<a name="dolphin-mlw-92.ps.Z" </a>
 
<b> <li> Speeding-up Logic Programs by Combining EBG and FOIL </b> <br> 
 
John M. Zelle and Raymond J. Mooney <br> 

<cite> Proceedings of the 1992 Machine Learning Workshop on Knowledge
Compilation and Speedup Learning</cite>, Aberdeen Scotland, July 1992. <p>

<blockquote>
This paper presents an algorithm that combines traditional EBL
techniques and recent developments in inductive logic programming to
learn effective clause selection rules for Prolog programs.  When these
control rules are incorporated into the original program, significant
speed-up may be achieved.  The algorithm produces not only EBL-like
speed up of problem solvers, but is capable of automatically
transforming some intractable algorithms into ones that run in
polynomial time.
</blockquote>

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<hr>
<address><!WA16><a href="http://www.cs.utexas.edu/users/estlin/">[e-mail]</a></address>