Probabilistic Graphical Models
The intersection of intelligent agents, web mining, and probabilistic graphical models in Tanya Braun's insightful work. Dive into the realm of cutting-edge technology and data analysis techniques that are shaping the future of artificial intelligence and information retrieval. Gain valuable insights into how these advanced methods are revolutionizing the way we interact with web-based content and harnessing the power of data for enhanced decision-making and user experiences.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
Intelligent Agents: Web-mining Agents Probabilistic Graphical Models Introduction Tanya Braun
Welcome Part of the module: Intelligent Agents (CS4514-KP12) Consists of two lectures per week First lecture per week on Autonomous Agents and Information Retrieval Second lecture per week on Probabilistic Graphical Models 2
Literature: Books Modelling and Reasoning with Bayesian Networks Adnan Darwiche Probabilistic Graphical Models Daphne Koller, Nir Friedman Artificial Intelligence: A Modern Approach (3rd ed.) Stuart Russell, Peter Norvig 3
Literature: Other than Books Two PhD theses (especially for Sections 1-3): Nima Taghipour: Lifted Probabilistic Inference by Variable Elimination. KU Leuven, 2013. https://lirias.kuleuven.be/1656026?limo=0 Tanya Braun: Rescued from a Sea of Queries: Exact Inference in Probabilistic Relational Models. UzL, 2020. https://www.ifis.uni-luebeck.de/~braun/Diss/Braun_diss.pdf Further research papers referenced in slides 4
Setting: Agent with Utilities 5 AIMA, Russell/Norvig
Probabilistic Graphical Models (PGMs) 1. Recap: Propositional modelling Factor model, Bayesian network, Markov network Semantics, inference tasks + algorithms + complexity 2. Probabilistic relational models (PRMs) Parameterised models, Markov logic networks Semantics, inference tasks 3. Lifted inference LVE, LJT, FOKC Theoretical analysis 4. Lifted learning Recap: propositional learning From ground to lifted models Direct lifted learning 5. Approximate Inference: Sampling Importance sampling MCMC methods 6. Sequential models & inference Dynamic PRMs Semantics, inference tasks + algorithms + complexity, learning 7. Decision making (Dynamic) Decision PRMs Semantics, inference tasks + algorithms, learning 8. Continuous Models Probabilistic soft logic: modelling, semantics, inference tasks + algorithms 6
