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teaching:gsoc2018 [2018/01/16 09:16] – [Google Summer of Code 2018] ahaidu | teaching:gsoc2018 [2018/01/21 20:29] – balintbe | ||
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====== Google Summer of Code 2018 ====== | ====== Google Summer of Code 2018 ====== | ||
~~NOTOC~~ | ~~NOTOC~~ | ||
+ | ===== pracmln ===== | ||
+ | |||
+ | //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:// | ||
+ | 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/ | ||
+ | |||
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===== 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 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios ==== | ||
+ | |||
+ | {{ : | ||
+ | |||
+ | **Main Objective: | ||
+ | able robots in a human environment to recognize objects in diffi- | ||
+ | cult and challenging scenarios. To achieve this the participant will | ||
+ | develop annotators for RoboSherlock that are particularly aimed at | ||
+ | object-hypotheses generation and merging. Generating a hypotheses | ||
+ | essentially means to generate regions/ | ||
+ | form a single object or object-part. In particular this entails the de- | ||
+ | velopment of segmentation algorithms for visually challenging scenes | ||
+ | or object properties, as the likes of transparent objects, or cluttered, | ||
+ | occluded scenes. The addressed scenarios include stacked, occluded | ||
+ | objects placed on shelves, objects in drawers, refrigerators, | ||
+ | ers, cupboards etc. In typical scenarios, these confined spaces also | ||
+ | bare an underlying structure, which will be exploited, and used as | ||
+ | background knowledge, to aid perception (e.g. stacked plates would | ||
+ | show up as parallel lines using an edge detection). Specifically we | ||
+ | would start from (but not necessarly limit ourselves to) the implemen- | ||
+ | tation of two state-of-the-art algorithms described in recent papers: | ||
+ | |||
+ | [1] Aleksandrs Ecins, Cornelia Fermuller and Yiannis Aloimonos, Cluttered Scene Segmentation Using the Symmetry Constraint, International Conference on Robotics and Automation(ICRA) 2016 | ||
+ | [2] Richtsfeld A., M ̈ | ||
+ | orwald T., Prankl J., Zillich M. and Vincze | ||
+ | M. - Segmentation of Unknown Objects in Indoor Environments. | ||
+ | IEEE/RSJ International Conference on Intelligent Robots and Sys- | ||
+ | tems (IROS), 2012. | ||
+ | |||
+ | **Task Difficulty: | ||
+ | | ||
+ | **Requirements: | ||
+ | |||
+ | **Expected Results:** Currently the RoboSherlock framework lacks good perception algorithms that can generate object-hypotheses in challenging scenarios(clutter and/or occlusion). The expected results are several software components based on recent advances in cluttered scene analysis that are able to successfully recognized objects in the scenarios mentioned in the objectives, or a subset of these. | ||
+ | |||
+ | 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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