adtool: Assisted and Automated Discovery for Complex Systems

We're pleased to introduce adtool (formerly, Automated Discovery Tool), a package assisted and automated discovery of patterns in the exploration of complex systems.

adtool is a software package developed in the Inria FLOWERS research team which provides an integrated solution for studying complex systems through curiosity-search methods, consisting of a user-friendly Web UI and an extensible Python library for user-defined experimentation systems and search algorithms.

Searching the configuration space of complex systems is often done manually, i.e., by a human who individually identifies interesting patterns or behaviors of the system. adtool thus assists in automating this exploratory phase of researching a new system which is theorized to be capable of interesting, yet unknown behavior. This is the case for many projects in the natural sciences and elsewhere. For example, physicists and chemists may use the tool study the emergence of novel structures and materials from a physical system, or digital artists and designers may use the tool to automatically generate or iterate on existing designs during the creative process.

Please note that this software is currently in an alpha stage of development: it is functional and has been used internally at Inria FLOWERS to study cellular automata since 2021, but may not have features which are convenient for different workflows.

The software was designed and maintained with contributions from Chris Reinke, Clément Romac, Matthieu Perie, Mayalen Etcheverry, Jesse Lin, and other collaborators in the FLOWERS team.

Summary

Installation

git clone https://github.com/flowersteam/adtool
cd adtool
conda create -n adtool python=3.11 -y
conda activate adtool
pip install .

Examples

Various examples are provided in the examples/ directory.

python3 run.py --config_file examples/grayscott/gray_scott.json

Available examples are:

• examples/grayscott/gray_scott.json - Discovering Gray-Scott diffusion patterns
  

• examples/particlelenia/particlelenia.json - Discovering particle lenia patterns
  

• examples/docking/docking.json - Minimalist pipeline to discover docking patterns in a protein pocket, with AutoDock Gnina ( gnina )
  

• examples/block_ca/block.json - Extension of single rotation block cellular automata to simulate reversible diffusion patterns
  

• examples/kuramoto/kuramoto.json - 1D Kuramoto base model
  

• examples/lenia/lenia_cppn.json - Discovering lenia patterns
  

• examples/flowlenia/flow_lenia_noise_small.json - Discovering flowlenia patterns with restricted hardware

• examples/flowlenia/flow_lenia_noise.json - Discovering flowlenia patterns initialized with noise

• examples/flowlenia/flow_lenia_cppn.json - Discovering flowlenia patterns initialized with CPPN

• examples/flashlenia/flashlenia.json - Cuda implementation of lenia to study initial condition sensitivity

• examples/stable_diffusion/stable_diffusion.json - Discovering variations around a stable diffusion inference

To execute examples, you need to install required dependencies:

pip install .[examples]

For the docking example, you need to install the rdkit package and download the gnina executable:

sudo apt install openbabel libxml2-dev libxslt-dev libopenbabel-dev swig
pip install plip --global-option=build_ext --global-option="-I/usr/local/include/openbabel3" --global-option="-L/usr/local/lib"
pip install rdkit
wget https://github.com/gnina/gnina/releases/download/v1.1/gnina -O examples/docking/systems/gnina
chmod +x examples/docking/systems/gnina

And also move replacements02_sa2.db downloaded here http://www.qsar4u.com/pages/crem.php to the examples/docking/maps/ directory.

Reloading

To reload a previous exploration, simply run the same previous commands but from an existing discoveries directory. Previous discoveries must be generated with the same system and maps.

Visualization

The tool provides a web interface to visualize the progression of the exploration. To start the web interface, run the following commands:

pip install .[visu]
cd examples/visu
python3 server.py --discoveries PATH_TO_DISCOVERIES

The web interface can be accessed at http://127.0.0.1:8765/.

Scientific Background: Curiosity Search

The high-dimensional phase space of a complex system poses many challenges to study. In particular, it is often desirable to explore the behavior space of such systems for interesting behaviors without knowing a priori the precise quantities to look for. As such, a class of algorithms based on intrinsic motivation or "curiosity" has been proposed in Reinke et al., 2020 and extended in e.g., Etcheverry et al., 2020 Such curiosity algorithms enable a system to automatically generate a learning curriculum from which it learns to explore its behavior space autonomously in search of interesting behaviors, originally proposed in the context of robotic agents learning to interact with their environment in an unsupervised manner, as in Oudeyer et al., 2007.

In practice, dealing with such ill-posed and/or subjective search tasks requires significant human oversight. For this reason, our adtool proposes a software package for both :

• the implementation of such experimental pipelines for arbitrary systems and search methods, and
• the human-supervised exploration of such systems.

The repo comes with an existing implementation of the Lenia system which can be explored using the curiosity search algorithms described.

Use ADtool as a package

pip install git+https://github.com/flowersteam/adtool

FAQ on the grayscott example

How can I change the size of the simulation?

This can be done directly by changing the configuration file here.

How can I change the number of steps for each simulation?

Possibly directly from the configuration file here.

How can I load another exploration algorithm?

From the configuration file by specifying the appropriate module path and name here. Other modules for this are defined here.

How can I change the parametric space of the simulation?

This is the 'parameter_map', introduced here in the configuration file. For example, it's possible to copy the default file serving as parameter_map defined here, then update the path to the new parameter_map in the used configuration file here.

How can I change the statistics used to characterize the final state of the simulation?

Same logic as 'parameter_map' but with 'behavior_map' here, an example of which is provided here. The current statistics are only calculated on the last state of the simulation, but nothing prevents considering all previous states.

How can I change the way the grayscott simulation operates?

For this, it's possible to start from the default system definition here, make modifications in a new file, and then update the path to the new system in the configuration file here.

What is the execution flow?

You can have a look at the simplified execution flow to understand a bit more about the different steps of the exploration.

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