- NEWS Initial release: Published after AAAI18.
- NEWS Updates on version 2: Mainly the refactoring. AAAI18 experiments still works.
This repository contains the source code of LatPlan.
- Asai, M.: 2019. Unsupervised Grounding of Plannable First-Order Logic Representation from Images (code not yet available)
- Accepted in ICAPS-2019, Learning and Planning Track.
- Asai, M.; Kajino, F: 2019. Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder (code not yet available)
- Accepted in ICAPS-2019, Learning and Planning Track.
- Asai, M.; Fukunaga, A: 2018. Classical Planning in Deep Latent Space: Breaking the Subsymbolic-Symbolic Boundary.
- Accepted in AAAI-2018.
- Asai, M.; Fukunaga, A: 2017. Classical Planning in Deep Latent Space: From Unlabeled Images to PDDL (and back).
- In Knowledge Engineering for Planning and Scheduling (KEPS) Workshop (ICAPS2017).
- In Cognitum Workshop at ICJAI-2017.
- In Neural-Symbolic Workshop 2017.
- Arxiv https://arxiv.org/abs/1705.00154
install.sh should install the required libraries on a standard Ubuntu rig.
It requires sudo several times. However Python packages are installed in the user directory.
Run do-everything.sh to generate the problem instances, train the network etc.
Python 3.5 or later is required.
On Ubuntu, prerequisites can be installed via launching ./install.sh .
The script compiles several C++ and Lisp binaries. However, it does not affect the
running system (e.g. installing a custom software under /usr/) except for
performing several apt-get install from the official ubuntu repository.
The applications installed by the script consists of:
mercurial g++ cmake make python flex bison g++-multilib— these are required for compiling Fast Downward.git build-essential automake libcurl4-openssl-dev— these are required for compiling [Roswell](http://roswell.github.io/). OSX users should usebrew install roswell.parallel— for running some scripts. This is not essential, but if you don’t parallel, give it a try! It is a super useful tool.python3-pip python3-pil— python packages.tensorflow keras h5py matplotlib progressbar2 timeout_decorator ansicolors scipy scikit-image— more python packages.bash-completion byobu htop moshThese are not necessary :) but I just use this script also for setting up the environment in a new machine.
- config.py, config_cpu.py
- keras/tensorflow configuration.
- model.py
- network definitions.
- strips.py
- (Bad name!) the program for training an SAE, and writes the propositional encoding of states/transitions to a CSV file.
- state_discriminator3.py
- The program for training an SD.
- action_autoencoder.py
- The program for training an AAE.
- action_discriminator.py
- The program for training an AD.
- fd-planner.py
- Latplan using AMA1.
- trivial-planner.py
- Latplan using AMA2.
- run_ama1_all.sh, run_ama2_all.sh
- Run all experiments.
- various sh files
- supporting scripts.
- util/
- contains general-purpose utility functions for python code.
- tests/
- test files, mostly the unit tests for domain generator/validator
- samples/
- where the learned results should go. Each SAE training results are stored in a subdirectory.
- puzzles/
- code for domain generators/validators.
- puzzles/*.py
- each file represents a domain.
- puzzles/model/*.py
- the core model (successor rules etc.) of the domain. this is disentangled from the images.
- problem-instances/
- where the input problem isntances / experimental results should go.
- helper/
- helper scripts for AMA1.
- (git submodule) planner-scripts/
- My personal scripts for invoking domain-independent planners. Not just Fast Downward.
- (git submodule) downward/
- My personally maintained version of Fast Downward, forked around 2015.