---
title: "Learning of Resource Allocation Strategies for Game Playing"
authors:
  - "Shaul Markovitch"
  - "Yaron Sella"
doi: ""
url: "https://ijcai.org/proceedings/1993/134"
pdf: "https://ijcai.org/proceedings/1993/0134.pdf"
venue: "Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence"
year: 1993
pages: "974-978"
track: "Machine Learning"
keywords:
---

# Learning of Resource Allocation Strategies for Game Playing

Shaul Markovitch, Yaron Sella

*Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence*, pages 974-978.

## Abstract

Human chess players exhibit a large variation in the amount of time they allocate for each move. Yet, the problem of devising resource allocation strategies for game playing did not receive enough attention . In this paper we present a framework for studying resource allocation strategies. We define allocation strategy and identify three major types of strategies: static, semi-dynamic, and dynamic. We then proceed to describe a method for learning semi-dynamic strategies from self generated examples. The method assigns classes to the examples based on the utility of investing extra resources. The method was implemented in the domain of checkers, and experimental results show that it is able to learn strategies that improve game-playing performance.