PPT-Markov Logic: Combining

Logic and Probability Parag Singla Dept of Computer Science amp Engineering Indian Institute of Technology Delhi Overview Motivation amp Background Markov logic Inference amp Learning Abductive. The fundamental condition required is that for each pair of states ij the longrun rate at which the chain makes a transition from state to state equals the longrun rate at which the chain makes a transition from state to state ij ji 11 Twosided stat T state 8712X action or input 8712U uncertainty or disturbance 8712W dynamics functions XUW8594X w w are independent RVs variation state dependent input space 8712U 8838U is set of allowed actions in state at time brPage 5br Policy action is function Sai. Zhang. , . Congle. Zhang. University of Washington. Presented. . by . Todd Schiller. Software bug localization: finding the likely buggy code fragments. A . software. system. (. source code. Parag. . Singla. Dept. of Computer Science and Engineering. Indian Institute of Technology, Delhi. Joint work with people at . University of Washington and IIT Delhi . Overview. Motivation. Markov logic. Hao. Wu. Mariyam. Khalid. Motivation. Motivation. How would we model this scenario?. Motivation. How would we model this scenario?. Logical Approach. Motivation. How would we model this scenario?. Logical Approach. notes for. CSCI-GA.2590. Prof. Grishman. Markov Model . In principle each decision could depend on all the decisions which came before (the tags on all preceding words in the sentence). But we’ll make life simple by assuming that the decision depends on only the immediately preceding decision. . Natarajan. Prasad . Tadepalli. *. Gautam. . Kunapuli. Jude . Shavlik. Dept of CS, University of Wisconsin-Madison. * School . of . EECS,Oregon. . State University. Learning Parameters for . Relational Probabilistic Models . Model Definition. Comparison to Bayes Nets. Inference techniques. Learning Techniques. A. B. C. D. Qn. : What is the. . most likely. . configuration of A&B?. Factor says a=b=0. But, marginal says. Fehringer. Seminar: Probabilistic Models for Information Extraction. by Dr. Martin . Theobald. and Maximilian . Dylla. . Based on Richards, M., and . Domingos. , P. (2006). Markov Logic Networks. 1. Tushar. . Khot. Joint work with . Sriraam. . Natarajan. , . Kristian. . Kersting. and . Jude . Shavlik. Sneak Peek. Present a method to learn structure and parameter for MLNs . simultaneously. Use functional gradients to learn many . Gordon Hazen. February 2012. Medical Markov Modeling. We think of Markov chain models as the province of operations research analysts. However …. The number of publications in medical journals . using Markov models. Parag. . Singla. & Raymond J. Mooney. Dept. of Computer Science. University of Texas, Austin. Motivation . [ Blaylock & Allen 2005] . Road Blocked!. Road Blocked!. Heavy Snow; Hazardous Driving. Relational. . Learning. . for. . NLP. William. . Y.. . Wang. William W. Cohen. Machine Learning Dept . and Language Technologies. . Inst.. joint work with:. Kathryn Rivard Mazaitis. Outline. Motivation. in Markov Logic using an RDBMS. Feng . Niu. , Chris . Ré. , . AnHai. Doan, and Jude . Shavlik. University of Wisconsin-Madison. One Slide Summary. 2. Machine Reading . is a DARPA program to capture knowledge expressed in free-form text.