Hipster: An Open Source Java

Library for Heuristic Search

Pablo Rodríguez-Mier, Adrián González-Sieira, Manuel Mucientes,

Manuel Lama, Alberto Bugarín.

Centro Singular de Investigación en Tecnoloxías da Información

Universidade de Santiago de Compostela

16 de junio de 2014citius.usc.es

Problem Solving as State Space Search - I

Many AI problems can be formulated as State Space Search.

We have a problem and we need to find a solution:

. Where do we start from?

. What is the solution of the problem?

. How can we assess the quality of a solution of a problem?

. Which actions or steps can we take in order to solve it?

. How can we estimate how far we are from the solution during the

search?

Examples:

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Problem Solving as State Space Search - II

In a nutshell, a state space search problem is defined in terms of states,

actions and transitions.

States: representation of a particular configuration of our problem.

. Initial state: where we start searching from.

. Goal state: state which represents a valid solution.

Actions: description of the possible actions or movements that we

can do from a given state.

Transitions: describe how actions lead from one given state to a

different state.

The solution to a problem is the sequence of actions that lead from the

initial state to the goal state.

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8-Puzzle game example - Step 1

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8-Puzzle game example - Step 2

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8-Puzzle game example - Step 3

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8-Puzzle game example - Step 4

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8-Puzzle game example - Step 5

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8-Puzzle game example - Step 6

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Motivation - I

Why did we create Hipster?

There is an important lack of standard implementations of

algorithms in Java.

Most implementations are neither extensible nor flexible:

. The search process cannot be controlled.

. Costs are usually floats or doubles, there is no way to use complex

custom costs.

. There is no way to handle more than one single goal.

. ...

Graph specific libraries are not always suitable for problem solving!

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Motivation - II

Project Goals

Iterative algorithms.

. while(search.hasNext()) search.next();

Flexible, extensible, reusable.

. Hipster.createAStar(problem).search(goal);

. Hipster.createIDA(problem).search(goal);

Powerful, simple, strong-typed API.

. Generic types→ compile-time type safety.

. Builders to assist the specification of search problems.

Highly-tested code.

. Unit tests, continuous integration in the cloud.

Open-Source.

. Permissive Apache 2.0 (GPL compatible) license.

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Currently implemented algorithms

Uninformed search (no heuristics)

. Depth First Search (DFS)

. Breadth First Search (BFS)

. Dijkstra’s algorithm

. Bellman-Ford

Informed search

. A* algorithm

. Iterative Deepening A* (IDA*)

. Anytime Dynamic A* (AD*)

Local search

. Hill-climbing.

. Enforced Hill-climbing.

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Solving 8-Puzzle with Hipster

8-Puzzle state space formulation example

States: 8-Puzzle represented as an array of numbers.

Actions: movements of the empty tile (RIGHT, LEFT, UP, DOWN).

Transitions: state × action→ state’ (board with the tiles swapped).

. Example: {5,4,0,7,2,6,8,1,3} × LEFT = {5,0,4,7,2,6,8,1,3}

Solution: minimal sequence of actions from initial to goal

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Solving 8-Puzzle with Hipster - Step 1

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Solving 8-Puzzle with Hipster - Step 2

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Solving 8-Puzzle with Hipster - Step 3

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Solving 8-Puzzle with Hipster - Step 4

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Solving 8-Puzzle with Hipster - Step 5

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Solving 8-Puzzle with Hipster - Step 6

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Solving 8-Puzzle with Hipster - Step 7

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Solving 8-Puzzle with Hipster - Step 8

Finally, we select an algorithm to perform the search until the goal state

is reached.

List<Integer> goalState = Arrays.asList(0,1,2,3,4,5,6,7,8);Hipster.createDijkstra(p).search(goalState);

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Different ways to search

Search can also be done in multiple ways.

Automatic search:

Hipster.createAStar(p).search(goalState);

Automatic search with event listeners:

Hipster.createAStar(p).search(new Algorithm.SearchListener() {public void handle(Object node){

...}

});

Fine-grained iterative search:

for (Node n : Hipster.createAStar(p)) {System.out.println("Current state is " + n.state());if (n.state().equals(goalState)) ...

}

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Java 8 lambda expressions

Boilerplate code (anonymous interface implementations) can be

removed using Java 8 lambda expressions:

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Hipster in Action

Solving 8-Puzzle problem with Hipster. 3 minute video tutorial:

http://youtu.be/ZPOhmnzOKA4

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Research projects using Hipster - I

Anytime Motion Replanning in State Lattices for Wheeled Robots.

Use of the Anytime Dynamic A* (AD*) for path search with

replanning.

Custom costs to evaluate the states:

. Collision probability.

. Path traversal time.

. Probability to reach safely the goal state.

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Research projects using Hipster - II

Automatic Web Service Composition

Backward A* search to minimize the number of services.

Heuristics to estimate the distance to the goal.

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Conclusions

A powerful but easy to use Java Library for using and developing

search algorithms.

Open Source, Apache 2.0 license.

Suitable for commercial and research projects.

Good for prototyping and testing algorithms.

Also suitable for teaching.

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Thanks for listening!

Questions?

Hipster Web Page

http://citiususc.github.io/hipster

16 de junio de 2014