---
title: "Flexible Learning of Problem Solving Heuristics Through Adaptive Search"
authors:
  - "Stephen F. Smith"
doi: ""
url: "https://ijcai.org/proceedings/1983/102"
pdf: "https://ijcai.org/proceedings/1983/0102.pdf"
venue: "Proceedings of the Eighth International Joint Conference on Artificial Intelligence"
year: 1983
pages: "422-424"
track: "Learning and Knowledge Acquisition"
keywords:
---

# Flexible Learning of Problem Solving Heuristics Through Adaptive Search

Stephen F. Smith

*Proceedings of the Eighth International Joint Conference on Artificial Intelligence*, pages 422-424.

## Abstract

Noting th at th e methods employed by ex istin g learnin g sys tems are often bound to th r: intended task domain and have little applicabi lity outside that do main, tli is paper co nsiders an :.\lterna ti ve learning sys tem design th at offers g reater fl ex ibility with o ut sacrifi c ing performance. An operational prototype, co nstru cted around a powerful adap tive searc h technique, is presented and appli ed to th e problem of acquiring problem so lving heuristics through experi ence. S om e performance results obt ai ned with th e system in a poker betting do main are reported and compared with th ose of a pr ev iously in vesti ga ted learning sys tem in th e same domain . It is seen th at comp arable levels of performance are achieved by th e two system s. despite the latter 's dependence on a considerable a mount of domain specific knowl edge for effective operation.