### PETER
CLARK:
PUBLICATIONS

###
Lazy Partial Evaluation: An Integration of Explanation-Based
Generalisation and Partial Evaluation

**Reference:**
P. Clark and R. Holte.
Lazy partial evaluation: An integration of explanation-based
generalisation and partial evaluation.
In D. Sleeman and P. Edwards, editors, *Proc. Ninth Int. Machine
Learning Conference (ML-92)*, pages 82-91, CA, 1992. Kaufmann.

**Abstract:**
In this paper we present lazy partial evaluation (LPE), a new learning
technique which is a hybrid of explanation-based generalisation (EBG)
and partial evaluation (PE). LPE operates in a similar way to EBG, except that
the work performed exploring failed proofs is also generalised and stored.
The equivalence of the set of explored proofs to the original goal
definition allows LPE to replace (rather than augment) the original
definition with them, speeding up proof search for future examples.
The resulting learning algorithm is significantly less computationally
expensive than EBG, while also avoiding the potentially vast memory
requirements of PE. It also removes the undesirable bias which EBG
introduces, where EBG's preference for reusing operational proofs may
result in a `poor' proof being selected. We describe LPE and compare its
performance with PE and EBG on two constraint satisfaction tasks.
Finally, we analyse the conditions in which each of the learning techniques
is most effective.

**PDF:**
http://www.cs.utexas.edu/users/pclark/papers/lpe.pdf

peter.e.clark@boeing.com