Software

AILibs

AILibs is a modular collection of Java libraries related to automated decision making. It's highlight functionalities are:

• Graph Search (jaicore-search): (AStar, BestFirst, Branch & Bound, DFS, MCTS, and more)

• Logic (jaicore-logic): Represent and reason about propositional and simple first order logic formulas

• Planning (jaicore-planning): State-space planning (STRIPS, PDDL), and hierarchical planning (HTN, ITN, PTN)

• Reproducible Experiments (jaicore-experiments): Design and efficiently conduct experiments in a highly parallelized manner.

• Automated Software Configuration (HASCO): Hierarchical configuration of software systems.

• Automated Machine Learning (ML-Plan): Automatically find optimal machine learning pipelines in WEKA or sklearn

All algorithms in AILibs are steppable, and their behavior can be analyzed via the algorithm inspector: jaicore-algorithminspector. For example, graph search algorithms send events that allow a graph visualization in the algorithm inspector.

DataDancer

DataDancer is a highly modular and flexible workbench and benchmark platform for developing and evaluating AutoML systems. It is based on the remote procedure calls extension of Google ProtoBuf (gRPC). Thereby, the individual modules can be implemented in different programming languages and still function as 

OpenML

OpenML is a platform fostering open research on machine learning, facilitating work on meta-learning, and busting myths in machine learning. Currently, I am one of the core developers of OpenML and responsible for the Java client of OpenML. If you are interested in OpenML and working on or with OpenML do not hesitate to get in touch with me.

Find out more about OpenML

PyExperimenter

PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms and in particular is designed to reduce the involved manual effort significantly. It is intended to be used by researchers in the field of artificial intelligence, but is not limited to those.

The empirical analysis of algorithms is often accompanied by the execution of algorithms for different inputs and variants of the algorithms (specified via parameters) and the measurement of non-functional properties. Since the individual evaluations are usually independent, the evaluation can be performed in a distributed manner on an HPC system. However, setting up, documenting, and evaluating the results of such a study is often file-based. Usually, this requires extensive manual work to create configuration files for the inputs or to read and aggregate measured results from a report file. In addition, monitoring and restarting individual executions is tedious and time-consuming.

These challenges are addressed by PyExperimenter by means of a single well defined configuration file and a central database for managing massively parallel evaluations, as well as collecting and aggregating their results. Thereby, PyExperimenter alleviates the aforementioned overhead and allows experiment executions to be defined and monitored with ease.

Academic ALPaCA

Acadamic ALPaCA stands for Academic Administration, knowLedge base, Paper organization and Collaboration Assistant and is an administrative tool for bringing better organization and collaboration to academia and other research labs.