---
title: "Learning General Search Control from Outside Guidance"
authors:
  - "Andrew R. Golding"
  - "Paul S. Rosenbloom"
  - "John E. Laird"
doi: ""
url: "https://ijcai.org/proceedings/1987/69"
pdf: "https://ijcai.org/proceedings/1987/0069.pdf"
venue: "Proceedings of the Tenth International Joint Conference on Artificial Intelligence"
year: 1987
pages: "334-336"
track: "Knowledge Acquisition"
keywords:
---

# Learning General Search Control from Outside Guidance

Andrew R. Golding, Paul S. Rosenbloom, John E. Laird

*Proceedings of the Tenth International Joint Conference on Artificial Intelligence*, pages 334-336.

## Abstract

The system presented here shows how Soar, an architecture for general problem solving and learning, can acquire general search-control knowledge from outside guidance. The guidance can be either direct advice about what the system should do, or a problem that illustrates a relevant idea. The system makes use of the guidance by first formulating an appropriate goal for itself. In the process of achieving this goal, it learns general search-control chunks. In the case of learning from direct advice, the goal is to verify that the advice is correct. The verification allows the system to obtain general conditions of applicability of the advice, and to protect itself from erroneous advice. The system learns from illustrative problems by setting the goal of solving the problem provided. It can then transfer the lessons it learns along the way to its original problem . This transfer constitutes a rudimentary form of analogy.