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IPyHOP

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IPyHOP is a Re-entrant Iterative GTPyHOP written in Python 3. PyHOP is an acronym for Python Hierarchical Ordered Planner.

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# [IPyHOP](https://github.com/YashBansod/IPyHOP/)

---
  
**If you use this repository please cite our work:**

```bibtex
@inproceedings{bansod2022htn,
  title={HTN Replanning from the Middle},
  author={Bansod, Yash and Patra, Sunandita and Nau, Dana and Roberts, Mark},
  booktitle={The International FLAIRS Conference Proceedings},
  volume={35},
  year={2022}
}
```

[IPyHOP](https://github.com/YashBansod/IPyHOP/) is a Re-entrant Iterative [GTPyHOP](https://github.com/dananau/GTPyhop) written in Python 3. [PyHOP](https://bitbucket.org/dananau/pyhop/src/master/) is an acronym for Python Hierarchical Ordered Planner.  
  
IPyHOP is a _Totally-Ordered Goal Task Network (GTN) Planner_ written in Python 3. It effectively plans like a re-entrant iterative version of GTPyHOP. However, IPyHOP produces a _solution tree_ (a task decomposition network) to accomplish a to-do list _T_ consisting of actions, tasks, and goals. The _solution tree_ preserves the hierarchy of the decompositions performed to obtain the _solution plan_. The _solution plan_ can be obtained by listing the actions in the _solution tree_ in a pre-ordered depth-first search manner. The plan presents the sequence of actions that accomplishes all of the items in _T_, in the order that they occur in _T_.  
  
IPyHOP can also re-enter any point in the task decomposition network and re-plan from there. Alternatively, it can be fed a partially solved task decomposition network to solve the planning problem.  
  
For more details on IPyHOP's planning algorithm please read the paper: [HTN Replanning from the Middle](https://journals.flvc.org/FLAIRS/article/download/130732/133891)

For examples of how to use it, see the example files that come with IPyHOP.
  
IPyHOP provides the following classes and functions:

* `state = State('foo')` tells IPyHOP to create an empty state object named 'foo'.  
    To put variables and values into it, you should do assignments such as `foo.var1 = val1`

* `methods = Methods()` tells IPyHOP to create an empty methods container.  
        To add tasks and associated task methods into it, you should use
        `methods.declare_task_methods(task_name, method_list)`.  
        To add tasks and associated goal methods into it, you should use
        `methods.declare_goal_methods(goal_name, method_list)`.  
        To add tasks and associated multigoal methods into it, you should use
        `methods.declare_multigoal_methods(goal_tag, method_list)`.  
        
* `actions = Actions()` tells IPyHOP to create an empty actions container.  
    To add actions into it, you should use `actions.declare_actions(action_list)`.  
    `declare_actions([a1, a2, ..., ak])` tells IPyHOP that a1, a2, ..., ak are all of the planning actions.  
    This supersedes any previous call to `declare_actions([a1, a2, ..., ak])`.

* `planner = IPyHOP(methods, actions)` tells IPyHOP to create a IPyHOP planner object.  
    To plan using the planner, you should use `planner.plan(state, task_list)`.  
  
* `planner.replan(state, fail_node_id)` can be used to re-plan from a failure node in the planner's solution tree.  
    `fail_node_id` is the id of the node in the solution tree that failed.  
    Let `fail_node` describe the action, task, or goal that caused the failure. Ex. ('move', 'a', 'b').  
    Then, to mark `fail_node` as a deterministic failure, you should blacklist it using `planner.blacklist_command(fail_node)`.  
  
* `planar_plot(planner.sol_tree)` can be used to visualize the solution tree graphically.  
  
* `planner.simulate(state)` can be used to deterministically simulate the plan generated by the planner from a given initial state.   
  
Please see the code doc strings for detailed descriptions of IPyHOP's classes and functions.  
  
---  
## Please see the [IPyHOP Wiki](https://github.com/YashBansod/IPyHOP/wiki) for more project details.  
---

IPyHOP

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