ActionGraph
ActionGraph is a symbolic AI agent for generating action plans based on preconditions and effects. This is loosely based on STRIPS approach (https://en.wikipedia.org/wiki/Stanford_Research_Institute_Problem_Solver). State variables are modeled as nodes; the actions represent edges/transitions from one state to another. Dijikstra's shortest path algorithm (A* but without the heuristic cost estimate) is used to generate a feasible, lowest cost plan.
Source: https://github.com/bharathra/ACTION_GRAPH
Usage:
from action_graph.agent import Agent
from action_graph.action import Action
class Drive(Action):
effects = {"driving": True}
preconditions = {"has_drivers_license": True, "tank_has_gas": True}
class FillGas(Action):
effects = {"tank_has_gas": True}
preconditions = {"has_car": True}
class RentCar(Action):
effects = {"has_car": True}
cost = 100 # dollars
class BuyCar(Action):
effects = {"has_car": True}
preconditions = {}
cost = 10_000 # dollars
if __name__ == "__main__":
world_state = {"has_car": False, "has_drivers_license": True}
goal_state = {"driving": True}
ai = Agent()
actions = [a(ai) for a in Action.__subclasses__()]
ai.load_actions(actions)
print("Initial State:", world_state)
ai.update_state(world_state)
print("Goal State: ", goal_state)
plan = ai.get_plan(goal_state)
# ai.execute_plan(plan)
1 maintainer
Metadata
1 maintainer
Release files for action-graph 1.3.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| action-graph-1.3.5.tar.gz | 8.8 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| action_graph-1.3.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 17.8 kB
Release files / action-graph-1.3.5.tar.gz
| Download URL | action-graph-1.3.5.tar.gz |
|---|---|
| Size | 8.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
80341d5afc49a204c82e860733002444539fb47568b3c367fe98043bb7778bbe
|
|
BLAKE2b-256 checksum How to use checksums |
1fc100827cbc8e5328931241edaa186b38a75894b8e12b08f0864692fac20204
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.10
|
Release files / action_graph-1.3.5-py3-none-any.whl
| Download URL | action_graph-1.3.5-py3-none-any.whl |
|---|---|
| Size | 9.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b2f32998a75601ca108845dbf4019c64acb3ffff6fa9cc583a0f32e16588d1ee
|
|
BLAKE2b-256 checksum How to use checksums |
79001ae916475a29a7fc1da63bbf7e1bb7e0c3108351fd179dc9c92a530188cd
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.10
|
Security for action-graph 1.3.5
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
