PPT-Adbuctive Markov Logic for Plan Recognition

Author : tatyana-admore | Published Date : 2019-06-21

Parag Singla amp Raymond J Mooney Dept of Computer Science University of Texas Austin Motivation Blaylock amp Allen 2005 Road Blocked Road Blocked Heavy Snow

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Adbuctive Markov Logic for Plan Recognition: Transcript

Parag Singla amp Raymond J Mooney Dept of Computer Science University of Texas Austin Motivation Blaylock amp Allen 2005 Road Blocked Road Blocked Heavy Snow Hazardous Driving. 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. Logic and Probability. Parag Singla. Dept. of Computer Science & Engineering. Indian Institute of Technology Delhi. Overview. Motivation & Background. Markov logic. Inference & Learning. Abductive. Kathryn Blackmond Laskey. Department of Systems Engineering and Operations Research. George Mason University. Dagstuhl. Seminar April 2011. The problem of plan recognition is to take as input a sequence of actions performed by an actor and to infer the goal pursued by the actor and also to organize the action sequence in terms of a plan structure. in Speech Recognition. Author. ：. Mark . Gales. 1. and Steve . Young. 2. Published. ：. 21 . Feb . 2008. . . Subjects. ：. Speech/audio/image/video . compression. Outline. Introduction. Architecture of an HMM-Based . 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. Presented by: Laura Slaughter. May 20, 2014. Summary. Context and Background. Brief overview of Information Extraction. Concept Recognition. Techniques. Tools. Evaluation. Exercises (focused on an example in medicine). 1. Speech Recognition and HMM Learning. Overview of speech recognition approaches. Standard Bayesian Model. Features. Acoustic Model Approaches. Language Model. Decoder. Issues. Hidden Markov Models. 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. 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. Living Technologies for Older Adults. Speaker: Parisa Rashidi. University of Florida. Introduction. Technologies, tools, infrastructure. Algorithms. Use Cases. Design Issues. Future. Outline. 2. Assisted living technologies for older adults, a.k.a. Srikar Nadipally. Hareesh . Lingareddy. What is Speech Recognition. A Speech Recognition System converts a speech signal in to textual representation. 3. of 23. Types of speech recognition. Isolated words. 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.

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