'Belief state' presentation slideshows

Probabilistic Inference

Probabilistic Inference. Agenda. Conditional probability Independence Intro to Bayesian Networks. Remember: Probability Notation Leaves Values Implicit. P(A ?B) = P(A)+P(B)- P(A ? B) means. P(A=a ? B=b) = P(A=a) + P(B=b) P(A=a ? B=b) For all a ?Val(A) and b ?Val(B).

761 views • 64 slides

Introducing Uncertainty (It is not the world that is imperfect, it is our knowledge of it) R&N: Chap. 3, Sect 3.6 +

Introducing Uncertainty (It is not the world that is imperfect, it is our knowledge of it) R&N: Chap. 3, Sect 3.6 + Chap. 13. So far, we have assumed that: World states are perfectly observable, ? the current state is exactly known

651 views • 54 slides

CS b659: Intelligent Robotics

CS b659: Intelligent Robotics. Planning Under Uncertainty. Offline learning (e.g., calibration). Prior knowledge (often probabilistic) . Sensors. Perception (e.g., filtering, SLAM). Decision-making (control laws, optimization, planning). Actions. Actuators. Dealing with Uncertainty.

519 views • 42 slides

Nondeterministic Uncertainty & Sensorless Planning

Nondeterministic Uncertainty & Sensorless Planning. Sensing error Partial observability Unpredictable dynamics Other agents. Uncertainty models. This class : Nondeterministic uncertainty f( x,u ) -> a set of possible successors Probabilistic uncertainty

554 views • 40 slides

Markov Models (Basics)

Markov Models (Basics). Markov Models. Markov Decision Process (MDP). No observation uncertainty, with decision. With observation uncertainty, no decision. Partially Observable Markov Decision Process (POMDP). Markov Chain (MC). Hidden Markov Model (HMM).

660 views • 39 slides

CS 8520: Artificial Intelligence

CS 8520: Artificial Intelligence. Robotics Paula Matuszek Spring, 2010. CSC 8520 Spring 2010. Paula Matuszek. Slides based in part on www.jhu.edu/virtlab/course-info/ei/ppt/robotics-part1.ppt and -part2.ppt. What is your favorite robot?. Wall-e: 2008.

516 views • 39 slides

Policies for POMDPs

Policies for POMDPs. Minqing Hu. Background on Solving POMDPs. MDPs policy: to find a mapping from states to actions POMDPs policy: to find a mapping from probability distributions (over states) to actions. belief state: a probability distribution over states

519 views • 38 slides

Oliver B. Martin Brian C. Williams {omartin, williams}@mit

Diagnosis as Approximate Belief State Enumeration for Probabilistic Concurrent Constraint Automata. Oliver B. Martin Brian C. Williams {omartin, williams}@mit.edu Massachusetts Institute of Technology. Michel D. Ingham michel.ingham@jpl.nasa.gov Jet Propulsion Laboratory.

486 views • 35 slides

Exact Mode Estimation for POMDPs based on Constraint Decomposition and Symbolic Encoding

Exact Mode Estimation for POMDPs based on Constraint Decomposition and Symbolic Encoding. Martin Sachenbacher July 1, 2003. Exact vs. Approximate ME. Problems of ME with incomplete belief state Dead ends (no solutions) Incorrect leading solutions Incorrect probabilities of solutions

474 views • 33 slides

Dialogue Modelling

455 views • 30 slides

Computer Science CPSC 502 Lecture 14 Partially Observable Markov Decision Processes

Computer Science CPSC 502 Lecture 14 Partially Observable Markov Decision Processes. Markov Models . Markov Chains. Hidden Markov Model. Partially Observable Markov Decision Processes (POMDPs). Markov Decision Processes (MDPs). Overview. Partially Observable Markov Decision Processes

492 views • 29 slides

Artificial Intelligence

469 views • 28 slides

Uncertainty in Sensing (and action)

Uncertainty in Sensing (and action). Agenda. Planning with belief states Nondeterministic sensing uncertainty Probabilistic sensing uncertainty. Belief State.

393 views • 28 slides

Exploiting Coordination Locales in DisPOMDPs via Social Model Shaping

Exploiting Coordination Locales in DisPOMDPs via Social Model Shaping. Pradeep Varakantham Singapore Management University. Joint work with J.Y.Kwak, M.Taylor , J. Marecki, P. Scerri, M.Tambe. Motivating Domains. Sensor Networks. Disaster Rescue. Characteristics of Domains:

298 views • 22 slides

Nonlinear Optimization for Optimal Control Part 2 Pieter Abbeel UC Berkeley EECS

Nonlinear Optimization for Optimal Control Part 2 Pieter Abbeel UC Berkeley EECS. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A A A. Outline. From linear to nonlinear Model-predictive control (MPC) POMDPs.

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Intelligent Systems (AI-2) Computer Science cpsc422 , Lecture 5 Jan, 15, 2014

Intelligent Systems (AI-2) Computer Science cpsc422 , Lecture 5 Jan, 15, 2014. Slide credit POMDP: C. Conati and P. Viswanathan. MDPs scalability.

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Decision Making Under Uncertainty Lec #4: Planning and Sensing

304 views • 19 slides

POMDP

422 views • 18 slides

CPS 270: Artificial Intelligence http://www.cs.duke.edu/courses/fall08/cps270/ Markov decision processes, POMDPs

Instructor: Vincent Conitzer. CPS 270: Artificial Intelligence http://www.cs.duke.edu/courses/fall08/cps270/ Markov decision processes, POMDPs. Warmup: a Markov process with rewards. We derive some reward from the weather each day, but cannot influence it. .6. s. 10. .1. .3. .4. .3. 8.

341 views • 17 slides

Classical Situation

Classical Situation. heaven. hell. World deterministic State observable. MDP-Style Planning. Policy Universal Plan Navigation function. [Koditschek 87, Barto et al. 89]. heaven. hell. World stochastic State observable. Stochastic, Partially Observable. heaven?. hell?. sign.

372 views • 15 slides