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teaching:gsoc2018 [2018/01/16 09:16] – [Google Summer of Code 2018] ahaidu | teaching:gsoc2018 [2018/01/22 09:49] – [RobCoG - Robot Commonsense Games] ahaidu | ||
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====== Google Summer of Code 2018 ====== | ====== Google Summer of Code 2018 ====== | ||
~~NOTOC~~ | ~~NOTOC~~ | ||
+ | ===== pracmln ===== | ||
- | {{ :teaching:gsoc:gsoc2016logo.jpg? | + | //pracmln// is a toolbox for statistical relational learning (SRL) and |
+ | reasoning and as such also includes tools for standard probabilistic | ||
+ | graphical models. //pracmln// is an implementation of Markov logic networks (MLN) | ||
+ | that supports efficient learning and inference in relational domains. | ||
+ | Its learning and inference engines are entirely written in the Python | ||
+ | programming language. Markov logic networks generalize both first-order | ||
+ | logic and probabilistic graphical models and have proven successful | ||
+ | in many real-world applications such as natural-language understanding | ||
+ | and robot perception. This makes MLNs one of the most general and most | ||
+ | powerful representation formalisms for uncertain knowledge. | ||
+ | |||
+ | //pracmln// was designed with the particular needs of technical systems in | ||
+ | mind. Our methods are geared towards practical applicability and can | ||
+ | easily be integrated into other applications. The availability of | ||
+ | graphical tools makes both learning or inference sessions a | ||
+ | user-friendly experience. Scripting support enables automation, and | ||
+ | a browser-based [[http:// | ||
+ | familiar with MLNs. | ||
+ | |||
+ | //pracmln// is an open-source project hosted on [[https:// | ||
+ | project page ([[http://www.pracmln.org]]) that provides extensive documentation | ||
+ | and tutorials that facilitate getting started with MLNs. It is provided as a pip | ||
+ | package in the Python package index ([[https:// | ||
+ | |||
+ | |||
+ | ===== RoboSherlock -- Framework for Cognitive Perception ===== | ||
+ | |||
+ | RoboSherlock is a common framework for cognitive perception, based on the principle of unstructured information management (UIM). UIM has proven itself to be a powerful paradigm for scaling intelligent information and question answering systems towards real-world complexity (i.e. the Watson system from IBM). Complexity in UIM is handled by identifying (or hypothesizing) pieces of | ||
+ | structured information in unstructured documents, by applying ensembles of experts for annotating information pieces, and by testing and integrating these isolated annotations into a comprehensive interpretation of the document. | ||
+ | |||
+ | RoboSherlock builds on top of the ROS ecosystem and is able to wrap almost any existing perception algorithm/ | ||
+ | |||
+ | ===== openEASE -- Web-based Robot Knowledge Service ===== | ||
+ | |||
+ | OpenEASE is a generic knowledge database for collecting and analyzing experiment data. Its foundation is the KnowRob knowledge processing system and ROS, enhanced by reasoning mechanisms and a web interface developed for inspecting comprehensive experiment logs. These logs can be recorded for example from complex CRAM plan executions, virtual reality experiments, | ||
+ | |||
+ | The OpenEASE web interface as well as further information and publication material can be accessed through its publicly available [[http:// | ||
+ | |||
+ | ===== RobCoG - Robot Commonsense Games ===== | ||
+ | |||
+ | [[http:// | ||
+ | |||
+ | The games are split into two categories: (1) VR/Full Body Tracking with physics based interactions, | ||
===== Proposed Topics ===== | ===== Proposed Topics ===== | ||
In the following, we list our proposals for the Google Summer of Code topics that contribute to the aforementioned open-source projects. | In the following, we list our proposals for the Google Summer of Code topics that contribute to the aforementioned open-source projects. | ||
+ | ==== Topic 1: Markov logic networks in Python ==== | ||
+ | |||
+ | [[http:// | ||
+ | |||
+ | |||
+ | **Main Objective: | ||
+ | Python. The main objective of this project is to port the | ||
+ | computationally heavy parts of the learning and inference algorithms to | ||
+ | [[http:// | ||
+ | compilation of Python modules to C libraries. Cython allows to add | ||
+ | static type declarations to Python, which can significantly speed up | ||
+ | execution (up to a factor of 1000 compared to regular Python code). | ||
+ | |||
+ | As most of the Python libraries for machine learning and scientific | ||
+ | computing (e.g. scikit-learn, | ||
+ | it is expected that the practical applicability of Markov logic networks | ||
+ | will substanially be pushed to more demanding real-world scenarios. | ||
+ | |||
+ | **Task Difficulty: | ||
+ | solving this task properly requires understanding and experience in | ||
+ | gradient-based optimization, | ||
+ | |||
+ | **Requirements: | ||
+ | language (CPython/ | ||
+ | (ideally SRL technques and logic) | ||
+ | |||
+ | **Expected Results:** The core components of pracmln, i.e. the learning | ||
+ | and inference modules are expected to be ported to the Cython language | ||
+ | exploiting the means of implementational optimization provided by | ||
+ | Cython. | ||
+ | |||
+ | **Contact: | ||
+ | |||
+ | |||
+ | ==== Topic 2: Felxible perception pipeline manipulation for RoboSherlock ==== | ||
+ | |||
+ | {{ : | ||
+ | |||
+ | **Main Objective: | ||
+ | |||
+ | **Task Difficulty: | ||
+ | | ||
+ | **Requirements: | ||
+ | |||
+ | **Expected Results:** an extension to RoboShelrock that allows splitting and joingin pipelines, executing them in parallel, merging results from multiple types of cameras etc. | ||
+ | |||
+ | Contact: [[team/ | ||
+ | |||
+ |
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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