Skip to content

Latest commit

 

History

History
71 lines (55 loc) · 2.8 KB

README.md

File metadata and controls

71 lines (55 loc) · 2.8 KB
Raw

Javadocs

JSearch

JSearch is a java framework for solving state space problems. It focuses on the following aspects:

  1. Proper documentation
  2. Clean and tested code
  3. Reusability
  4. Performance

Various AI problem solving techniques are bundled, suited for varying types of problems:

  • A*
  • Beamsearch
  • Depth first search
  • IDA* (iterative deepening A*)
  • Iterative deepening search
  • Recursive best first search
  • SMA* (simplified memory-bounded A*)

Installation

You can use JSearch as a Maven dependency:

<dependency>
   <groupId>com.github.dieterdepaepe</groupId>
   <artifactId>jsearch-core</artifactId>
   <version>1.0.1</version>
</dependency>
<!-- Contains examples, not needed for actual usage -->
<dependency>
   <groupId>com.github.dieterdepaepe</groupId>
   <artifactId>jsearch-examples</artifactId>
   <version>1.0.0</version>
</dependency>

Or build it yourself:

git clone https://github.com/DieterDePaepe/JSearch.git
cd JSearch
# optional: checkout a version
# git checkout 1.0.1
mvn package
# jars are found in the target folders

Usage

It is up to the user to define the details of the problem to be solved. This comes down to creating implementations for the following interfaces (more details available in the javadoc):

  • SearchNode: the representation of all information relating to the problem for that state
  • Problem environment (no interface): the representation of all information shared between all states
  • SearchNodeGenerator: defines the possible transitions from one state to another
  • Heuristic (optional but recommended): an estimator of the remaining cost for a state

The examples folder contains implementations for the N-Puzzle problem.

Once defined, you pick an algorithm (an implementation of Solver) and start the search using the Solvers helper class. For examples, refer to the examples folder.

Alternative Libraries

OptaPlanner is another optimisation library that is definitely worth checking out. It is more extensive than JSearch and more actively developed. A short comparison is given:

  • JSearch focusses on constructive search, so it more suited for problems where the search path is important (Eg: pathfinding in a graph, finding the best way to solve a Rubics Cube,...) or where a garanteed optimal solution is needed.
  • OptaPlanner focusses on local search, it is more suited for problems with many variables or problems where it is difficult to determine the quality of a partial solution. It is definitely more scalable than JSearch for large, complex problems.