Types of Artificial Intelligence
Then, you will explore predictive analytics and machine learning in the model, towards prototyping a digital urban twin. This project is a collaboration with Arcadis , the leading global design and consultancy firm for natural and built assets. Integration of renewable energy in power systems is a potential source of uncertainty, because renewable generation is variable and may depend on changing and highly uncertain weather conditions.
An example is power generated by wind turbines.
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Although renewable wind energy is clean and cheap, it may be intermittent and its availability is uncertain and difficult to predict. An example is the charging process of an electric vehicle, which often does not have to be charged immediately, as long as the available power in the battery is sufficient to reach a destination. In our group we develop efficient planning techniques to automatically coordinate deferrable loads, such that electricity is used when renewable supply is available.
Various MSc projects can be formulated in this domain.
Examples include, but are not limited to, coordination of charging electric vehicles, scheduling household appliances, electricity usage within network constraints and predicting electricity demand and supply. Contact: Erwin Walraven , Matthijs Spaan. Planning under uncertainty is a technique for enabling agents to successfully plan their decisions in domains with stochastic transitions and partial observability, e.
In the Algorithmics group a considerable body of expertise, algorithms and software is present regarding these methods.
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- Thesis: AI Optimization in Logistics (f/m/d);
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This allows for defining challenging projects focused on improving state-of-the-art algorithms with a high potential for publications. Contact: Matthijs Spaan. Reinforcement learning is a branch of machine learning focusing on agents that are able to automatically learn how they should act in their environment, and has been applied for coordination of multiagent systems in several real-world domains.
For instance, reinforcement learning has been used for intelligent control of multiple traffic lights in the urban area, as well as optimization of traffic flow on highways. Other examples in the area of transportation and logistics include air traffic management and automated unloading of ships in a harbor. Typically, a real-world problem is modeled as reinforcement learning problem and then evaluated through realistic simulations.
Interested in a Bachelor or Master Thesis in the Field of Artificial Intelligence?
MSc projects may focus on the application of reinforcement learning algorithms to realistic domains, but also allow for more fundamental research related to reinforcement learning and decision making under uncertainty. In our group expertise and software are present for reinforcement learning in transportation, traffic and logistics domains.
However, students may also choose their own problem domain to define an interesting thesis project. The automotive industry jointly with many software companies and automotive suppliers are boosting their efforts to transform the future of transportation by making self-driving cars become a reality. Testing the interactions between automated and human driven vehicles in real life is complex as limited number of automated vehicles drive on our road network.
Therefore, testing these interactions in a driving simulator environment in the first stage is more realistic.
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However, a suitable driving simulator environment which can simulate human driven vehicles and automated vehicles does not exist. Your aim in this thesis is to develop the driving simulator environment, including the integration of existing behavioural models for humans as well as automated vehicles from the literature into the driving simulator environment. Constraint Programming is known as a powerful approach to combinatorial optimisation problems. Part of the "Holy Grail" of computing is for the user to state the problem and the computer to solve it.
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However, acquiring a suitable CP model still requires human expertise. This thesis project explores the recent uses of machine learning to acquire improved CP models. Reinforcement Learning RL has revolutionised what computers can do: game playing, speech translation, car driving, making artwork. Given their success, deep RL techniques are now being applied to combinatorial optimisation problems, such as vehicle routing .
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- Dissertations for Artificial Intelligence.
- IN9495 – Advanced Topics in Artificial Intelligence for Intelligent Systems.
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These kind of problems are important in business, science and policy making. Traditionally their study has been called Operations Research OR . The aim of this project is understand how RL can be used as an alternative and as a complement to already successful OR models and methods. Relevant questions could for example be:. Contact: N. Artificial Intelligence This project is a specific instance of the project area Machine learning coupled with state-of-the-art OR algorithms.
You will look at the solving process of modern MIP solvers such as Gurobi. There is recent work on injecting ML into specific aspects of MIP solving [1,2,3,4]: you will look at using RL, possibly in combination with other forms of learning. This project is a collaboration with Mathematics. Nemhauser, Bistra N. In the past, machine learning and combinatorial optimisation components of a solving pipeline are viewed as independent black-boxes: predict the input parameters and then optimise.
However, conventional ML metrics, such as mean-square error, are not necessarily indicative for the outcome of the optimisation. There are three approaches: a direct methods which interact with the optimisation problem during ML training by considering a relaxed version of the combinatorics, b semi-direct methods which take into account features of the optimisation problem but do not directly interact during training, and c indirect methods which ignore the optimisation problem standard ML approaches. This project can explore either a direct or a semi-direct method.
There is a good potential to publish an academic paper.
signndorconno.tk This project is a collaboration with VUB. You will build on that initial study to see what classes of neural networks can be modelled. What is feasible? Which discrete optimisation solvers?
How deep can we go? Complexity analysis? Constraints 23 3 : NetLogo is a functional multi-paradigm programming language for agent-based social simulation. NetLogo is widely used is the social sciences. It is open source , based on Scala. NetLogo has some features for static and dynamic code analysis and a simple profiler. It has no features for unit testing or advance code analysis. This project studies the requirements, feasibility and implementation of a prototype for NetLogo.
This project is a collaboration with TPM faculty. The topics listed above are only a sample of possible projects; fully up-to-date information can be obtained by contacting our staff members:. To apply for a MSc place, please read and follow the formal guidelines of Delft University of Technology. People Menu openen.
Projects Menu openen. Education Menu sluiten. Fundamental Challenges in AI Algorithms. Walraven Spaan Reinforcement learning for transportation, traffic and logistics E. Walraven Spaan Planning under uncertainty Spaan Multi-party multi-issue negotiation versus matching C.
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Bliek de Weerdt Machine-learning-based state space simplification in sequential decision-making models G. Neustroev de Weerdt Long versus short-term decision making G.