adtool: Assisted and Automated Discovery for Complex Systems

We're pleased to introduce adtool (formerly, Automated Discovery Tool), a software for 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. For more details on the development of adtool, see the following usage and technical section.

In the above demo, the tool is used to discover life-like propagating patterns in a cellular automata simulation, showcasing how a researcher can specify a series of experiments and monitor their results through both the Web UI and an integrated Jupyter notebook.

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

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. The Python API described in the technical documentation can be used to add custom systems and (optionally) search algorithms.

Installation

The application uses Docker, which should be installed manually following the official instructions. The application has been "real-world tested" on MacOS and Linux, so on Windows, installation under WSL (Windows Subsystem for Linux) should be preferred.

To install, simply clone the prod branch of the repository, which is where the stable version is kept, via e.g.,

git clone https://github.com/flowersteam/adtool.git --branch prod

After installation, the appropriate environment variables should be set by creating a file services/prod/.env with the environment variables appropriate for your setup. A sample is provided at services/prod/sample.env

Basic Usage

Running start_app.py --mode prod will launch the Docker containers, from which the UI can be accessed by default at http://localhost:4200

Developer's Documentation

For development work on the Python libraries, we recommend starting a new conda environment with

conda create -n adtool python=3.11 poetry=1.* -y

or, if you are running on the new Macbooks with M1 processors

CONDA_SUBDIR=osx-64 conda create -n adtool python=3.11 poetry=1.* -y

Please see the online documentation at https://developmentalsystems.org/adtool/.

Please note that as the software is currently in an alpha stage as of January 2023, breaking changes to the API may occur. This is due to the current progress towards two key developmental milestones:

• Streamlining of the API, allowing checkpointing and restoring of experiments with arbitrary data, à la Git
• Implementation of a human interface for interaction with the search process itself, allowing custom human intervention at each step taken

Due to the software currently being in an early stage of development, there may be questions which are not immediately answered by documentation, and you may instead direct them to Jesse Lin who is happy to respond.