Category: Artificial Intelligence

Graph-Based Recommendation Systems

Graph-Based Recommendation Systems use graphs to model relationships between users, items, and other entities. In these systems, users and items are represented as nodes, and their interactions, such as likes or purchases, are shown as edges connecting them. By analysing the structure of these graphs, the system can find patterns and suggest items to users…

Graph Embedding Propagation

Graph embedding propagation is a technique used to represent nodes, edges, or entire graphs as numerical vectors while sharing information between connected nodes. This process allows the relationships and structural information of a graph to be captured in a format suitable for machine learning tasks. By propagating information through the graph, each node’s representation is…

Model-Free RL Algorithms

Model-free reinforcement learning (RL) algorithms help computers learn to make decisions by trial and error, without needing a detailed model of how their environment works. Instead of predicting future outcomes, these algorithms simply try different actions and learn from the rewards or penalties they receive. This approach is useful when it is too difficult or…

Multi-Agent Coordination

Multi-agent coordination is the process where multiple independent agents, such as robots, software programs, or people, work together to achieve a shared goal or complete a task. Each agent may have its own abilities, information, or perspective, so they need to communicate, share resources, and make decisions that consider the actions of others. Good coordination…

Safe Reinforcement Learning

Safe Reinforcement Learning is a field of artificial intelligence that focuses on teaching machines to make decisions while avoiding actions that could cause harm or violate safety rules. It involves designing algorithms that not only aim to achieve goals but also respect limits and prevent unsafe outcomes. This approach is important when using AI in…

Hierarchical Policy Learning

Hierarchical policy learning is a method in machine learning where a complex task is divided into smaller, simpler tasks, each managed by its own policy or set of rules. These smaller policies are organised in a hierarchy, with higher-level policies deciding which lower-level policies to use at any moment. This structure helps break down difficult…

Off-Policy Evaluation

Off-policy evaluation is a technique used to estimate how well a new decision-making strategy would perform, without actually using it in practice. It relies on data collected from a different strategy, called the behaviour policy, to predict the outcomes of the new policy. This is especially valuable when testing the new strategy directly would be…

Value Function Approximation

Value function approximation is a technique in machine learning and reinforcement learning where a mathematical function is used to estimate the value of being in a particular situation or state. Instead of storing a value for every possible situation, which can be impractical in large or complex environments, an approximation uses a formula or model…

Policy Iteration Techniques

Policy iteration techniques are methods used in reinforcement learning to find the best way for an agent to make decisions in a given environment. The process involves two main steps: evaluating how good a current plan or policy is, and then improving it based on what has been learned. By repeating these steps, the technique…