PPT-Efficient Inference Methods for Probabilistic Logical Models

Sriraam Natarajan Dept of Computer Science University of WisconsinMadison TakeAway Message Inference in SRL Models is very hard This talk Presents 3 different yet related. Probabilistic Model Computationally more efficient models are developed based on probabilistic approach including discriminant analysis models, probit analysis models and the most popular logit analys in human semantic memory. Mark . Steyvers. , Tomas L. Griffiths, and Simon Dennis. 소프트컴퓨팅연구실. 오근현. TRENDS in Cognitive Sciences vol. . 10, . no. . 7, 2006. Overview . Relational models of memory. Probabilistic Model Computationally more efficient models are developed based on probabilistic approach including discriminant analysis models, probit analysis models and the most popular logit analys Temporal (Sequential) Process. A temporal process is the evolution of system state over time. Often the system state is hidden, and we need to reconstruct the state from the observations . Relation to Planning:. Michael Hicks. Piotr (Peter) Mardziel. University of Maryland, College Park. Stephen Magill. Galois. Michael Hicks. UMD. Mudhakar. . Srivatsa. IBM TJ Watson. Jonathan Katz. UMD. Mário. . Alvim. UFMG. (Markov Nets). (Slides from Sam . Roweis. ). Connection to MCMC:. . . MCMC requires sampling a node given its . markov. blanket. . Need to use P(. x|MB. (x)). . . For . Bayes. nets MB(x) contains more. 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. a Probabilistic . Lexical . Inference System. . Eyal Shnarch. ,. . Ido . Dagan, Jacob . Goldberger. PLIS - Probabilistic Lexical Inference System. 1. /34. The . entire talk in a single sentence. Thinking and Everyday Life. Michael K. Tanenhaus. Inference in an uncertain world. Most of what we do, whether consciously or unconsciously involves probabilistic inference. Decisions. Some are conscious:. Machine Learning @ CU. Intro courses. CSCI 5622: Machine Learning. CSCI 5352: Network Analysis and Modeling. CSCI 7222: Probabilistic Models. Other courses. cs.colorado.edu/~mozer/Teaching/Machine_Learning_Courses. Thesis defense . 4/5/2012. Jaesik Choi. Thesis Committee: . Assoc. Prof. Eyal Amir (Chair, Director of research). Prof. Dan Roth. . Prof. Steven M. Lavalle. Prof. David Poole (University of British Columbia). Rodrigo de Salvo Braz. Ciaran O’Reilly. Artificial Intelligence Center - SRI International. Vibhav Gogate. University of Texas at Dallas. Rina Dechter. University of California, Irvine. IJCAI-16. , . Bart Selman. selman@cs.cornell.edu. Module: Knowledge, Reasoning, and Planning. Logical Agents. Model . Theoretic Semantics. Entailment . and Proof Theory. R&N: Chapter 7. Logical agents:. . . Bart Selman. selman@cs.cornell.edu. Module: Knowledge, Reasoning, and Planning. Part 1. Logical Agents. R&N: Chapter 7. A Model-Based Agent. Requires: Knowledge and Reasoning. Knowledge and Reasoning .