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pygoap's Introduction

pygoap v.3
requires python 2.x

PyGoap is a small library for designing AI in python.  The basic library is based off of a well-known idea of using graph searches to to create realistic agents in real-time.  Behavior is determined in real-time and is extremely open ended and well suited for emergent behaviors.

Agents, or npcs, can be programmed very simply with an easy to understand api.  Check the 'npcs' folder and read through actions.py to get an idea of how it works.

This library is not complete, but is working as-is.  The demo gives you a simple diagram of the game world and the debugging information near the bottom lets you know what is going on 'in the heads' of the agents.  Note: the demo requires pygame.



I've build AI Agents around the concept of Actions and ActionBuilders.

Actions can be subclassed from a few different action types.
ActionBuilders will search a blackboard and return a list of actions that can be performed.



If you are unfamiliar with GOAP, I invite you to do some research online, it may help you to understand this module.



you can watch it work by running "test.py".


many thanks to opengameart.org for providing and hosting the tileset used in the project.  tiles for the map can be found at:
http://opengameart.org/content/worldmapoverworld-tileset
under the CC-BY 3.0 license.

have fun.

pygoap's People

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pygoap's Issues

test.py not showing any movement or decisions

Hello bitcraft,
when trying to run your pyGOAP/test.py I don't see any movements. The map loads fine and the red blocks show up, but then nothing happens until the test hits the step limit.

pyGOAP test py Screenshot - 02 04 2013 - 15:55:49

There are no errors but I uploaded the console output.

Could you give me a hint on where to look for bug?

I need a GOAP-like behaviour engine for a real robot (at high level only). Do you think using your pyGOAP for that would be a suitable idea?

Kind regards,
Felix

Python 3 Support

I have in the works real python 3.3 support plus lots of fixes and enhancements. The main items addressed are planning speed, precept propagation, and goal/action development. The code sitting in the repository now will be the last version that is python 2 compatible. I will have a new commit and updated wiki in about one week.

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