PPT-CS 478 - Learning Rules

1 Learning Sets of Rules CS 478 Learning Rules 2 Learning Rules If Color Red and Shape round then Class is A If Color Blue and Size large then Class is B If Shape square then Class is A. Jonathan . Mugan. & Benjamin . Kuipers. Presented by. daniel. Hough. Learning Distinctions and Rules in a Continuous World through Active Exploration. The Challenge. To build a robot which learns its environment like children do.. There are a number of reasons why our young people do not learn. Relationships. One . thing we rarely discuss is the value and importance of human connection. James Comer. “No . significant learning can take place without a significant . 1. Learning Sets of Rules. CS 478 - Learning Rules. 2. Learning Rules. If (Color = Red) and (Shape = round) then Class is A. If (Color = Blue) and (Size = large) then Class is B. Natural . and intuitive hypotheses. Advanced Internet Systems. Slot-Filling Architectures. Dan Weld. Logistics. Datasets. Stored on Amazon. Computing . $100 EC2 credit per student. You’ll need to set up your own account. Teams. 8-10 teams of 2-3 people. Narasimhan. . Rampalli. , Frank Yang, . AnHai. Doan. @. WalmartLabs. & UW-Madison. Presenter: Jun . Xie. , @. WalmartLabs. . Chimera: Large-Scale Classification using Machine Learning, Rules, and . “This is not a game, it is training for war”. A . game for boys from twelve years of age to one hundred and fifty and for that more intelligent sort of girl who likes boy’s games and . books. A . Decision Trees. Gavin Brown. www.cs.man.ac.uk/~gbrown. Recap: threshold classifiers. height. weight. Q. Where is a good threshold?. 10 20 30 40 50 60. 1. 0. Also known as “decision stump”. From Decision . Holland (1986): “. Classifier systems … rule-based systems with general mechanisms to process rules in parallel, for the adaptive generation of rules, and for testing the effectiveness of existing rules.”. Matthew Fisher. What is general game learning?. Learning to play games you have never . seen. Reading . or listening to the rules. Asking questions about the rules. Learning the . rules by playing (ex. Tetris). 2. Chalmers University of Technology, Sweden. The computer program. Alice In . Wonderland (AIW). AIW:. Is a system for learning and reasoning in arbitrary symbolic . systems. Combines . elements of cognitive modeling and . Copyright © Andrew W. Moore. Density Estimation – looking ahead. Compare it against the two other major kinds of models:. Regressor. Prediction of. real-valued output. Input. Attributes. Density. Estimator. Inês Dutra. University. . of. Porto, CRACS & . INESC-Porto. LA. . Houssam. . Nassif. , . David . Page. , . Jude. . Shavlik. , . Roberta . Strigel. , . Yirong. . Wu. , . Mai. . Elezabi. . machine learning implies that a machine will learn how to do something new . but this is . not quite accurate – what is it that the machine is to learn?. is there a process in place and the machine needs to learn domain knowledge? . What is general game learning?. Learning to play games you have never . seen. Reading . or listening to the rules. Asking questions about the rules. Learning the . rules by playing (ex. Tetris). Figuring out the objective.