AMDN

.gitignore

@@ -12,4 +12,6 @@ __init__.pyc
 generated_testing_files/
 new_domain.pddl
 new_prob.pddl
-test_model.json
+test_model.json
+results.txt
+html/

CONTRIBUTING.md

@@ -5,11 +5,13 @@
 ### Installing
 
 Install macq for development by cloning the repository and running
-`pip install .[dev]`
+`pip install -e .[dev]`
 
 We recommend installing in a virtual environment to avoid package version
 conflicts.
 
+**Note: `tarski` requires [`clingo`](https://potassco.org/clingo/) be installed to work.**
+
 ### Formatting
 
 We use [black](https://black.readthedocs.io/en/stable/) for easy and consistent
@@ -34,3 +36,12 @@ report, run `pytest --cov=macq --cov-report=html`, and open `htmlcov/index.html`
 in a browser. This will provide detailed line by line test coverage information,
 so you can identify what specifically still needs testing.
 
+### Generating Docs
+To generate the HTML documentation, run `pdoc --html macq --config latex_math=True`.
+
+During development, you can run a local HTTP server to reference/see live
+changes to the documentation: `pdoc --http : macq --config latex_math=True`.
+
+*Note: `--config latex_math=True` is required to properly render the latex found
+in many extraction techniques' documentation.*
+

README.md

@@ -18,7 +18,7 @@ This library is a collection of tools for planning-like action model acquisition
 ## Usage <a name="usage" />
 ```python
 from macq import generate, extract
-from macq.observation import IdentityObservation
+from macq.observation import IdentityObservation, AtomicPartialObservation
 
 # get a domain-specific generator: uses api.planning.domains problem_id/
 # generate 100 traces of length 20 using vanilla sampling
@@ -39,50 +39,6 @@ trace.actions
 trace.get_pre_states(action) # get the state before each occurance of action
 trace.get_post_states(action) # state after each occurance of action
 trace.get_total_cost()
-
-######################################################################
-# Model Extraction - OBSERVER Technique
-######################################################################
-observations = traces.tokenize(IdentityObservation)
-model = extract.Extract(observations, extract.modes.OBSERVER)
-model.details()
-
-Model:
-  Fluents: at stone stone-03 location pos-04-06, at stone stone-01 location pos-04-06, at stone stone-02 location pos-05-06, at stone stone-06 location pos-07-04, at stone stone-11 ...
-  Actions:
-    push-to-goal stone stone-04 location pos-04-05 location pos-04-06 direction dir-up location pos-04-04 player player-01:
-      precond:
-        at player player-01 location pos-04-06
-        at stone stone-04 location pos-04-05
-        clear location pos-05-06
-        ...
-      add:
-        at stone stone-04 location pos-04-04
-        clear location pos-04-06
-        at-goal stone stone-04
-        at player player-01 location pos-04-05
-      delete:
-        at stone stone-04 location pos-04-05
-        clear location pos-04-04
-        at player player-01 location pos-04-06
-  ...
-######################################################################
-# Model Extraction - SLAF Technique
-######################################################################
-traces = generate.pddl.VanillaSampling(problem_id = 123, plan_len = 2, num_traces = 1).traces
-observations = traces.tokenize(PartialObservabilityToken, method=PartialObservabilityToken.random_subset, percent_missing=0.10)
-model = Extract(observations, modes.SLAF)
-model.details()
-
-Model:
-  Fluents: clear location pos-06-09, clear location pos-02-05, clear location pos-08-08, clear location pos-10-05, clear location pos-02-06, clear location pos-10-02, clear location pos-01-01, at stone stone-05 location pos-08-05, at stone stone-07 location pos-08-06, at stone stone-03 location pos-07-04, clear location pos-03-06, clear location pos-10-06, clear location pos-10-10, clear location pos-05-09, clear location pos-05-07, clear location pos-02-07, clear location pos-09-01, at stone stone-06 location pos-04-06, clear location pos-02-03, clear location pos-07-05, clear location pos-09-10, clear location pos-06-05, at stone stone-01 location pos-05-04, clear location pos-02-10, clear location pos-06-10, clear location pos-11-03, at stone stone-11 location pos-06-08, at stone stone-08 location pos-04-07, clear location pos-01-10, clear location pos-07-03, clear location pos-02-11, clear location pos-03-01, clear location pos-06-02, clear location pos-03-02, clear location pos-11-01, clear location pos-06-03, clear location pos-08-04, clear location pos-09-11, at stone stone-09 location pos-08-07, clear location pos-09-07, clear location pos-06-07, clear location pos-10-01, clear location pos-11-09, clear location pos-03-05, clear location pos-07-06, clear location pos-05-05, at stone stone-12 location pos-07-08, clear location pos-10-03, clear location pos-11-11, clear location pos-10-09, clear location pos-02-01, clear location pos-02-02, clear location pos-01-02, at stone stone-02 location pos-06-04, clear location pos-03-10, clear location pos-05-10, clear location pos-07-10, clear location pos-09-05, clear location pos-07-09, clear location pos-05-03, clear location pos-10-11, clear location pos-01-03, at stone stone-04 location pos-04-05, clear location pos-07-02, clear location pos-09-06, clear location pos-10-07, clear location pos-01-09, clear location pos-03-07, clear location pos-04-04, clear location pos-01-11
-  Actions:
-    move player player-01 direction dir-left location pos-05-02 location pos-06-02:
-      precond:
-      add:
-      delete:
-        (clear location pos-05-02)
-        (at player player-01 location pos-06-02)
 ```
 
 ## Coverage <a name="coverage"></a>
@@ -92,9 +48,9 @@ Model:
 - [x] [Learning Planning Operators by Observation and Practice](https://aaai.org/Papers/AIPS/1994/AIPS94-057.pdf) (AIPS'94)
 - [ ] [Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains](https://www.sciencedirect.com/science/article/pii/B9781558603356500192) (ICML'94)
 - [ ] [Learning Probabilistic Relational Planning Rules](https://people.csail.mit.edu/lpk/papers/2005/zpk-aaai05.pdf) (ICAPS'04)
-- [ ] [Learning Action Models from Plan Examples with Incomplete Knowledge](https://www.aaai.org/Papers/ICAPS/2005/ICAPS05-025.pdf) (ICAPS'05)
+- [x] [Learning Action Models from Plan Examples with Incomplete Knowledge](https://www.aaai.org/Papers/ICAPS/2005/ICAPS05-025.pdf) (ICAPS'05)
 - [ ] [Learning Planning Rules in Noisy Stochastic Worlds](https://people.csail.mit.edu/lpk/papers/2005/zpk-aaai05.pdf) (AAAI'05)
-- [ ] [Learning action models from plan examples using weighted MAX-SAT](https://www.sciencedirect.com/science/article/pii/S0004370206001408) (AIJ'07)
+- [x] [Learning action models from plan examples using weighted MAX-SAT](https://www.sciencedirect.com/science/article/pii/S0004370206001408) (AIJ'07)
 - [ ] [Learning Symbolic Models of Stochastic Domains](https://www.aaai.org/Papers/JAIR/Vol29/JAIR-2910.pdf) (JAIR'07)
 - [x] [Learning Partially Observable Deterministic Action Models](https://www.aaai.org/Papers/JAIR/Vol33/JAIR-3310.pdf) (JAIR'08)
 - [ ] [Acquisition of Object-Centred Domain Models from Planning Examples](https://ojs.aaai.org/index.php/ICAPS/article/view/13391) (ICAPS'09)
@@ -113,7 +69,7 @@ Model:
 - [ ] [Learning STRIPS Action Models with Classical Planning](https://arxiv.org/abs/1903.01153) (ICAPS'18)
 - [ ] [Learning Planning Operators from Episodic Traces](https://aaai.org/ocs/index.php/SSS/SSS18/paper/view/17594/15530) (AAAI-SS'18)
 - [ ] [Learning action models with minimal observability](https://www.sciencedirect.com/science/article/abs/pii/S0004370218304259) (AIJ'19)
-- [ ] [Learning Action Models from Disordered and Noisy Plan Traces](https://arxiv.org/abs/1908.09800) (arXiv'19)
+- [x] [Learning Action Models from Disordered and Noisy Plan Traces](https://arxiv.org/abs/1908.09800) (arXiv'19)
 - [ ] [Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Black Box Simulators](https://arxiv.org/abs/2002.01080) (ICML-WS'20)
 - [ ] [STRIPS Action Discovery](https://arxiv.org/abs/2001.11457) (arXiv'20)
 - [ ] [Learning First-Order Symbolic Representations for Planning from the Structure of the State Space](https://arxiv.org/abs/1909.05546) (ECAI'20)

docs/extract/amdn.md

@@ -0,0 +1,13 @@
+# Usage
+
+```python
+from macq import generate, extract
+
+print(model.details())
+```
+
+**Output:**
+```text
+```
+
+# API Documentation

docs/extract/arms.md

@@ -0,0 +1,77 @@
+# Usage
+
+```python
+from macq import generate, extract
+from macq.trace import PlanningObject, Fluent, TraceList
+from macq.observation import PartialObservation
+
+def get_fluent(name: str, objs: list[str]):
+    objects = [PlanningObject(o.split()[0], o.split()[1]) for o in objs]
+    return Fluent(name, objects)
+
+traces = TraceList()
+generator = generate.pddl.TraceFromGoal(problem_id=1801)
+
+generator.change_goal(
+    {
+        get_fluent("communicated_soil_data", ["waypoint waypoint2"]),
+        get_fluent("communicated_rock_data", ["waypoint waypoint3"]),
+        get_fluent(
+            "communicated_image_data", ["objective objective1", "mode high_res"]
+        ),
+    }
+)
+traces.append(generator.generate_trace())
+
+generator.change_goal(
+    {
+        get_fluent("communicated_soil_data", ["waypoint waypoint2"]),
+        get_fluent("communicated_rock_data", ["waypoint waypoint3"]),
+        get_fluent(
+            "communicated_image_data", ["objective objective1", "mode high_res"]
+        ),
+    }
+)
+traces.append(generator.generate_trace())
+
+observations = traces.tokenize(PartialObservation, percent_missing=0.60)
+model = extract.Extract(
+    observations,
+    extract.modes.ARMS,
+    upper_bound=2,
+    min_support=2,
+    action_weight=110,
+    info_weight=100,
+    threshold=0.6,
+    info3_default=30,
+    plan_default=30,
+)
+
+print(model.details())
+```
+
+**Output:**
+```text
+Model:
+  Fluents: (at rover rover0 waypoint waypoint2), (have_soil_analysis rover rover0 waypoint waypoint2), (have_soil_analysis rover rover0 waypoint waypoint3), ...
+  Actions:
+    (communicate_image_data rover waypoint mode objective lander waypoint):
+      precond:
+        calibrated camera rover
+        have_rock_analysis rover waypoint
+        communicated_rock_data waypoint
+        channel_free lander
+        at_soil_sample waypoint
+        at_rock_sample waypoint
+      add:
+        calibrated camera rover
+        at rover waypoint
+        have_image rover objective mode
+        channel_free lander
+        communicated_image_data objective mode
+      delete:
+        calibrated camera rover
+    ...
+```
+
+# API Documentation

docs/extract/extract.md

@@ -0,0 +1,9 @@
+# Usage
+
+## Debugging
+Include the argument `debug=True` to `Extract` to enable debugging for any
+extraction technique.
+
+*Note: debugging output and interfaces are unique to each method.*
+
+# API Documentation

docs/extract/observer.md

@@ -0,0 +1,37 @@
+# Usage
+
+```python
+from macq import generate, extract
+from macq.observation import IdentityObservation
+
+traces = generate.pddl.VanillaSampling(problem_id=123, plan_len=20, num_traces=100).traces
+observations = traces.tokenize(IdentityObservation)
+model = extract.Extract(observations, extract.modes.OBSERVER)
+
+print(model.details())
+```
+
+**Output:**
+```text
+Model:
+  Fluents: at stone stone-03 location pos-04-06, at stone stone-01 location pos-04-06, at stone stone-02 location pos-05-06, at stone stone-06 location pos-07-04, at stone stone-11 ...
+  Actions:
+    push-to-goal stone stone-04 location pos-04-05 location pos-04-06 direction dir-up location pos-04-04 player player-01:
+      precond:
+        at player player-01 location pos-04-06
+        at stone stone-04 location pos-04-05
+        clear location pos-05-06
+        ...
+      add:
+        at stone stone-04 location pos-04-04
+        clear location pos-04-06
+        at-goal stone stone-04
+        at player player-01 location pos-04-05
+      delete:
+        at stone stone-04 location pos-04-05
+        clear location pos-04-04
+        at player player-01 location pos-04-06
+  ...
+```
+
+# API Documentation

docs/extract/slaf.md

@@ -0,0 +1,28 @@
+# Usage
+
+```python
+from macq import generate, extract
+from macq.observation import AtomicPartialObservation
+
+traces = generate.pddl.VanillaSampling(problem_id=123, plan_len=2, num_traces=1).traces
+observations = traces.tokenize(AtomicPartialObservation, percent_missing=0.10)
+model = Extract(observations, extract.modes.SLAF)
+print(model.details())
+```
+
+**Output:**
+```text
+Model:
+  Fluents: clear location pos-06-09, clear location pos-02-05, clear location pos-08-08, clear location pos-10-05, ...
+  Actions:
+    move player player-01 direction dir-left location pos-05-02 location pos-06-02:
+      precond:
+      add:
+      delete:
+        (clear location pos-05-02)
+        (at player player-01 location pos-06-02)
+        ...
+    ...
+```
+
+# API Documentation

docs/index.md

@@ -0,0 +1,17 @@
+# Usage Documentation
+
+## Trace Generation
+- [VanillaSampling](extract/observer/#usage)
+
+## Tokenization
+- [IdentityObservation](extract/observer#usage)
+- [AtomicPartialObservation](extract/slaf#usage)
+
+## Extraction Techniques
+- [Observer](extract/observer#usage)
+- [SLAF](extract/slaf#usage)
+- [ARMS](extract/arms#usage)
+- [AMDN](extract/amdn#usage)
+
+
+# API Documentation

macq/__init__.py

@@ -0,0 +1,3 @@
+"""
+.. include:: ../docs/index.md
+"""

macq/extract/__init__.py

@@ -1,14 +1,15 @@
-from .model import Model
-from .extract import Extract, modes, IncompatibleObservationToken, SLAF
 from .learned_fluent import LearnedFluent
 from .learned_action import LearnedAction
+from .model import Model, LearnedAction
+from .extract import Extract, modes
+from .exceptions import IncompatibleObservationToken
+from .model import Model
 
 __all__ = [
+    "LearnedAction",
+    "LearnedFluent",
     "Model",
     "Extract",
     "modes",
     "IncompatibleObservationToken",
-    "LearnedAction",
-    "SLAF",
-    "LearnedFluent",
-]
+]