action-graph 1.2.1

Autonomous agent for task/action planning and execution

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.

The interfaces for this library are similar to https://github.com/agoose77/GOAP.

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)