PPT-Probabilistic Plan Recognition

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. However the exact compu tation of association probabilities jk in JPDA is NPhard where jk is the probability that th observation is from th track Hence we cannot expect to compute association probabilities in JPDA exactly in polynomial time unless N Component-Based Shape Synthesis. Evangelos. . Kalogerakis. , . Siddhartha . Chaudhuri. , . Daphne . Koller. , . Vladlen. . Koltun. Stanford . University. Goal: generative model of shape. Goal: generative model of shape. Tyler Lu and Craig . Boutilier. University of Toronto. Introduction. New communication platforms can transform the way people make group decisions.. How can . computational social choice . realize this shift?. (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. 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. Madhu Sudan. . MIT CSAIL. 09/23/2009. 1. Probabilistic Checking of Proofs. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. Can Proofs Be Checked Efficiently?. Shou-pon. Lin. Advisor: Nicholas F. . Maxemchuk. Department. . of. . Electrical. . Engineering,. . Columbia. . University,. . New. . York,. . NY. . 10027. . Problem: . Markov decision process or Markov chain with exceedingly large state space. Taisuke. Sato. Tokyo Institute of Technology. Problem. model-specific learning algorithms. Model 1. EM. VB. MCMC. Model 2. Model n. .... .... EM. 1. EM. 2. EM. n. Statistical machine learning is a . 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:. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. T. he . cost of computing an exact representation of the . configuration . space of a . free-flying 3D object, or a multi-joint . articulated object . is . often . prohibitive. But . very fast algorithms exist that can check if . Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . 2. Question to Consider. What are the key challenges police officers face when dealing with persons in behavioral crisis?. 3. Recognizing a. Person in Crisis. Crisis Recognition. 4. Behavioral Crisis: A Definition.