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teaching:mfe:ia [2013/04/03 10:56] stuetzle [Stochastic local search algorithms for weighted maximum clique problems.] |
teaching:mfe:ia [2014/04/17 20:33] mdorigo |
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| * [[http://iridia.ulb.ac.be/~fmascia|Franco Mascia (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~fmascia|Franco Mascia (IRIDIA)]] | ||
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| + | ===== Feature Extraction and Automatic Algorithm Selection. ====== | ||
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| + | The performance of (Stochastic Local Search) algorithms for a given problem depends on the algorithm design and on the setting of the algorithm's parameter. Given a heterogeneous set of instances for a given problem a good algorithm design (or parameter configuration) for one instance is not necessary the best design for all instances. On the contrary a tuning of an algorithm on a specific family of similar instances may affect negatively its performance on other families of instances. | ||
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| + | The thesis will focus on devising automatic methods for extracting features from the instances, select the relevant features, and learning (in the framework of multi-class classification) the | ||
| + | relationship, if there is one, between the instances features and the best algorithm for the instance. The results will be instrumental for algorithm selection or the creation of portfolios of complementary algorithms suitable for large sets of diverse instances for a given problem. | ||
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| + | Required skills: good knowledge of C or C++ programming and of a scripting language (e.g., python); good knowledge of machine learning methods would also be helpful. | ||
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| + | * Contacts : | ||
| + | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| + | * [[http://iridia.ulb.ac.be/~fmascia|Franco Mascia (IRIDIA)]] | ||
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| * Contact: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]] and Carlo Pinciroli (IRIDIA) | * Contact: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]] and Carlo Pinciroli (IRIDIA) | ||
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| - | ===== Self-organized visual coverage in a swarm of robots ===== | ||
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| - | Systems composed of several inter-connected cameras are already a reality in our everyday lives. The prime application of such systems is video-surveillance, but the possibilities off ered by multiple-camera systems can extend to other interesting scenarios, such as environment mapping, 3D shape-reconstruction and object recognition. In all these scenarios, the problem of finding the right | ||
| - | position of a set of cameras in order to maximize the visual field, or the amount of information available, is not always a simple one. Furthermore, systems consisting of cameras in a fixed position present obvious issues of robustness and flexibility. | ||
| - | Multi-robots systems can provide an interesting mean to overcome this issues. Robots navigating in the enviroment can change their position as a result of changes in the enviroment or in the overall system's objective. A centralized control solution for these systems is still not a desirable one, as it introduces a single point of failure and it can suff er from performance issues. | ||
| - | The Swarm Robotics paradigm o ffers a valid approach to the design of a multiple camera system. In this project, we want to study the possibility to develop a control strategy that enables a swarm of robots to position themselves into an unknown environment, maximizing the area covered by their visual fields, while relying only on their image processing system and on local communication. | ||
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| - | Required skills: The candidate should be acquainted with C/C++ programming and have a | ||
| - | working knowledge of the English language. | ||
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| - | * Contact: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]] and Alessandro Stranieri (IRIDIA) | ||
| ===== Automatic fitness function definition in evolutionary robotics ===== | ===== Automatic fitness function definition in evolutionary robotics ===== | ||