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teaching:mfe:ia [2015/04/11 17:44] stuetzle [Applications of the Multi-objective ACO framework] |
teaching:mfe:ia [2016/03/14 16:02] mdorigo [Design of a holonomic drive system] |
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| - | ====== MFE 2013-2014 : Intelligence Artificielle ====== | + | ====== MFE 2015-2016 : Intelligence Artificielle ====== |
| ===== Introduction ===== | ===== Introduction ===== | ||
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| * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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| - | ===== Optimising Ant Colony Algorithms for Performance ====== | ||
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| - | Ants have inspired a number of computational techniques and among the most successful is ant colony optimization (ACO). ACO is an optimization technique that can be applied to tackle a wide variety of computational problems that arise in computer science, telecommunications, and engineering. | ||
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| - | The goal of this project is to improve the performance of ACO algorithms by investigating and testing various implementation techniques: intrinsic functions (MMX/SSE floating-point operations), CPU cache effects, or GPU programming. | ||
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| - | Required skills: knowledge of C programming. Some knowledge about computer architecture. | ||
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| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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| * [[http://iridia.ulb.ac.be/~lperez|Leslie Perez (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~lperez|Leslie Perez (IRIDIA)]] | ||
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| - | ===== Stochastic Local Search heuristics for solving NP-complete puzzles. ====== | ||
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| - | This project is about single player games (puzzles) and the design of algorithms for tackling hard combinatorial optimisation problems. | ||
| - | Example puzzles are: [[http://en.wikipedia.org/wiki/Light_Up|Light Up]], [[http://en.wikipedia.org/wiki/Mastermind_(board_game)|Mastermind]], [[http://en.wikipedia.org/wiki/Minesweeper_(video_game)|Minesweeper]], etc. | ||
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| - | The student will learn how to design and implement a Stochastic Local Search algorithm to solve NP-complete puzzles. The student will also learn how to analyse the performaces of the algorithm and perform statistically sound comparisons with the other algorithms available in literature. | ||
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| - | Required skills: good knowledge of C or C++ programming. | ||
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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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| * Contacts : | * Contacts : | ||
| * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| + | * [[http://code.ulb.ac.be/iridia.people.php?id=1393|Alberto Franzin (IRIDIA)]] | ||
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| - | ===== Collective Decision Making with Heterogeneous Agents ===== | ||
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| - | Swarm robotics is an interesting approach to the coordination of hundreds of robots as it promotes the realization of systems which are scalable, robust and flexible. | ||
| - | The master thesis will study how to provide a swarm system with the cognitive capability of collective decision making. | ||
| - | Each agent has partial knowledge of the available alternatives and of their quality estimate, however the swarm, as a whole, is able to decide for the best option. Recently, numerous works have studied strategies and algorithms to implement this process in distributed systems (often taking inspiration from biology, e.g., bees or cockroaches behaviour). One of the common characteristic of these works is that all the agents of the swarm has the same behaviour. In the Master Thesis project, the student will study how heterogeneity influences the global outcome. We will consider heterogeneity both in the individual behaviour (for instance, robots can estimate different option characteristics) and in the interaction network. | ||
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| - | In practice, the student is supposed to (i) model the collective decision making problem | ||
| - | (ii) design and implement multi-agents simulations, and (iii) analyse and discuss the obtained | ||
| - | results. Depending on the student skills and preferences, the work can focus more on theoretical | ||
| - | aspects, thus favouring the modelling and analysis of the problem, or can be more practical, | ||
| - | thus centring the thesis on the multi-agent implementation part. Possibly, a more practical | ||
| - | thesis could result (depending on the student skills) in the implementation of a real world | ||
| - | demonstrator with a swarm of up to 100 robots. | ||
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| - | * Contacts : [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo]] and Andreagiovanni Reina (IRIDIA) | ||
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| - | ===== Automatic fitness function definition in evolutionary robotics ===== | + | ===== Swarm construction: development of remote monitoring software for intelligent structures ===== |
| - | Evolutionary robotics is a fascinating approach to the design of robot controllers that takes inspiration from natural evolution. | + | S-blocks are dynamically reconfigurable blocks used for swarm-based autonomous construction applications. When two or more S-blocks are assembled they are capable of communicating with each other over a near field communication (NFC) wireless interface. The goal of this master thesis is to develop software to monitor (and control) the blocks in an intelligent structure remotely over the auxiliary Zigbee-based wireless interface. As only one block in the structure is fitted with this wireless interface, it is required that the other blocks communicate with the PC, via routing messages through the block-to-block NFC interfaces. This will require the software on the S-Blocks to be enhanced to use preemptive task swapping, to allow multiple blocks to communicate with each other simultaneously. |
| - | In order to obtain a robot that is able to perform a desired task, the evolutionary robotics approach considers a population of robots that evolves in time. Each robot is characterized by a genotype that defines somehow its behavior. Each robot is evaluated according to a fitness function that measures the ability of the robot to perform the desired task. Robots with a low fitness are eliminated. Robots with a high fitness remain in the population and generate offsprings -- e.g., robots with a similar genotype obtained via mutation and/or cross-over. Through this process, generation by generation, the evolutionary robotics approach is able to obtain robots that present higher and higher fitness and that are therefore able to perform the desired task more and more effectively. | + | Required skills: The candidates should understand low level computer concepts such as: interrupts, timers, and registers, have some experience with C/C++ programming, and have a working knowledge of the English language. |
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| - | One of the main open problems in evolutionary robotics is that the definition of an appropriate fitness function is a very complex, labor-intensive, and time-consuming activity that requires the attention of an expert researcher. | + | |
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| - | The goal of this master thesis is to devise an automatic method to define a fitness function in order to obtain a robot that is able to perform a desired task. This automatic method will be based on machine learning and metaheuristic algorithms. In particular, it will draw ideas from the fields of reinforcement learning and of on-line adaptation of parameters in optimization algorithms. | + | |
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| - | Required skills: The candidates should be acquainted with C/C++ programming and have a working knowledge of the English language. | + | |
| - | * Contact: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Vito Trianni (IRIDIA) | + | * Contact: [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo]] (IRIDIA) |
| - | ===== Evolution of Modular Controllers for Simulated and Real Robots ===== | + | ===== Design of a holonomic drive system for autonomous robots in a swarm ===== |
| - | The goal of this master thesis is investigating how modularity in a robot controller can influence the quality of the behaviours obtained through artificial evolution. | + | Unlike a differential drive system, a holonomic drive system has the advantage of being able to move in any direction at a given instant. The goal of this master thesis is to design and evaluate the performance of a holonomic drive system. The drive system will be assembled from a combination of off-the-shelf components and 3D printed parts. In order to evaluate the drive system, close loop controllers need to be designed and evaluated in C/C++. |
| - | Similarly to the nervous system that can be divided in central and peripheral, the project will study a modular architecture for neural network controllers. The peripheral modules encode the information coming from the sensory subsytems or going to the motor apparatus. The central system encodes the behavioural rules that map sensations to actions. The project will study methods to develop the peripheral modules by maximising the information transfer from the sensory input and to the motor output, on the basis of measures derived from Information Theory. | + | |
| - | The project will involve experimental activities with both simulated and real robots, and will investigate both individual and collective behaviours. | + | |
| - | Required skills: The candidates should be acquainted with C/C++ programming and have a working knowledge of the English language. | + | Required skills: The candidates should have some experience with programming in C/C++, and some experience with 3D modelling (preferably Solidworks). The candidates should be able to use basic kinematics to solve simple physics problems, and have a working knowledge of the English language. |
| - | * Contact: [[http://iridia.ulb.ac.be/~vtrianni|Vito Trianni]], Marco Dorigo (IRIDIA) | + | * Contact: [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo]] (IRIDIA) |
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| - | ===== Automatic fine-tuning of an evolutionary multi-objective framework ===== | ||
| - | The goal of this project is to explore the possibilities of using automatic configuration tools for fine-tuning an existing [[http://paradiseo.gforge.inria.fr/index.php?n=Paradiseo.MOEO|evolutionary multi-objective framework]]. The student will learn about automatic configuration tools, evolutionary algorithms for multi-objective optimization problems and analysis and comparison of multi-objective algorithms. | ||
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| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~manuel|Manuel López-Ibáñez (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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