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teaching:mfe:ia [2011/03/23 16:07] mdorigo [Self-organized task allocation in swarm robotics] |
teaching:mfe:ia [2011/03/23 17:06] mdorigo |
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| This project is about single player games (puzzles) and the design of algorithms for tackling hard combinatorial optimisation problems. | This project is about single player games (puzzles) and the design of algorithms for tackling hard combinatorial optimisation problems. | ||
| - | Example puzzles are: <a href="http://en.wikipedia.org/wiki/Light_Up">Light Up</a>, <a href="http://en.wikipedia.org/wiki/Mastermind_(board_game)">Mastermind</a>, <a href="http://en.wikipedia.org/wiki/Minesweeper_(video_game)">Minesweeper</a>, etc. | + | 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. |
| 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. | 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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| * Contact: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Eliseo Ferrante, Ali Emre Turgut (IRIDIA) | * Contact: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Eliseo Ferrante, Ali Emre Turgut (IRIDIA) | ||
| - | ===== A comparison of decision-making strategies for adaptive foraging in swarm robotics ===== | ||
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| - | Group of social insects are able to efficiently find the (shortest) path to the a food source and even to differentiate between the quality of two food sources. Studies with ants showed that this mechanism is driven by the perception of stimuli from chemical substances like pheromone. Moreover ants are able to collectively modify their choices if there are changes in the environment, that is, if a source becomes better than another. These ideas have been a source of inspiration for several algorithms in swarm robotics which solves a similar problem (retrieval of objects) by using different types of stimuli such as the encounter rate of objects. | ||
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| - | The goal of this project is to perform a study on how to solve a foraging task in which robots have to choose between staying at the nest or go foraging for different energy sources. The optimal strategy might change over time. What happens if all the robots go to the best source? Will these "traffic jams" slow the process? Is it possible to avoid this problem? What if source quality changes over time? The study will be conducted only in simulation and will concern comparing different approaches and different metrics to measure stimuli. | ||
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| - | Required skills: The candidates should be acquainted with C++ programming and have a working knowledge of the English language. | ||
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| - | * Contact: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Eliseo Ferrante, Manuele Brambilla (IRIDIA) | ||
| ===== Kaleidoscope: Creating temporal motion patterns in a swarm of robots ===== | ===== Kaleidoscope: Creating temporal motion patterns in a swarm of robots ===== | ||
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| - | + | ===== Automatic fitness function definition in evolutionary robotics ===== | |
| + | Evolutionary robotics is a fascinating approach to the design of robot controllers that takes inspiration from natural evolution. | ||
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| + | 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. | ||
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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. | ||
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| + | * Contact: [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari]], Marco Dorigo, Vito Trianni (IRIDIA) | ||
| ===== Simulation et optimisation de trafic routier ===== | ===== Simulation et optimisation de trafic routier ===== | ||