The Heuristic Search Research Framework

Abstract

The Heuristic Search Research Framework is a software framework for conducting studies of heuristic search algorithms. The framework is more than a set of building blocks that facilitate the production of flexible and efficient implementations of heuristic search algorithms. The intended uses that distinguish this framework are: (1) gaining of insights into properties of search algorithms through a variety of means, (2) easy production of examples that can be used both for conference presentations and to teach heuristic search to students and (3) using policy-based design for building a taxonomy of implemented algorithmic variants. The paper presents the motivation behind the framework’s design and provides a short description of the framework’s features with examples. The paper is supplemented by extensive documentation that includes a video demo.

Introduction

Heuristic search is a general problem solving method in Artificial Intelligence [1]. It is used in many fields including robotics [2], constraint satisfaction problems [3], digital entertainment [4], multiagent pathfinding [5], [6], [7], puzzles [8], [9], [10], automated planning [11], [12], [13] and various well known NP-complete problems [14].

The quest for faster heuristic search results in a large number of new algorithms and variants of existing algorithms each year. Typically, a researcher needs to implement his algorithm, explore its properties and evaluate its performance relative to other algorithms for a large number of different problem instances and parameter settings.

These activities present many challenges as detailed below. The Heuristic Search Research Framework is motivated by the need to address these challenges and facilitate effective research of heuristic search algorithms.