This is a Python implementation of GOAP (Goal-Oriented Action Planning), a generic architecture for autonomous AI.

GOAPy was written in 2014 and updated in 2019 to work with Python 3. It predates other libraries of the same name and is not to be confused with GOApy.

GOAPy is simple in design and can be set up with only a few lines of code. It supports weighted actions and has debug text for tracking planner outcomes.

Created by Jeff Orkin, GOAP is employed by a variety of AAA game titles, known for its high level of flexibility and ease-of-use. Those interested in learning more should check out Jeff's website.

This library is currently in development.

See example.py.

By taking the current state of the AI (e.g.: Hungry? Tired?) and their desired state (e.g.: Not tired) a "plan" is generated to take the AI from their current state to their goal state by considering a list of actions. By attaching weights to these actions, GOAP is capable of producing highly dynamic results. In this case, the AI will discover that in order to sleep, they must first find and consume food, potentially leading to a complex course of action that involves going shopping, cooking, ordering pizza, taking a shower, etc.

The MIT License (MIT)

Copyright (c) 2014 Luke Martin (flags)

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