'Belief space' presentation slideshows

CULTURAL SWARMS: KNOWLEDGE-DRIVEN PROBLEM SOLVING IN SOCIAL SYSTEMS

CULTURAL SWARMS: KNOWLEDGE-DRIVEN PROBLEM SOLVING IN SOCIAL SYSTEMS. Robert G. Reynolds. Department of Computer Science, Wayne State University Museum of Anthropology, University of Michigan. The Emergence of Social Intelligence and Socially Motivated Problem Solving.

1.01k views • 86 slides

Solving POMDPs through Macro Decomposition

Solving POMDPs through Macro Decomposition. Larry Bush Tony Jimenez Brian Bairstow. Outline. Introduction to POMDPs Brian Bairstow Demonstration of POMDPs Larry Bush Approximating POMDPs with Macro Actions Tony Jimenez. Introduction to POMDPs. Introduction to POMDPs

817 views • 68 slides

Partially Observable Markov Decision Process (Chapter 15 & 16)

909 views • 67 slides

CSE-573 Artificial Intelligence

677 views • 53 slides

CSE-473 Artificial Intelligence

CSE-473 Artificial Intelligence. Partially-Observable MDPS ( POMDPs). Classical Planning. What action next?. Static. Predictable. Environment. Fully Observable . Discrete . Deterministic . Perfect. Percepts. Actions. Stochastic Planning (MDPs, Reinforcement Learning).

543 views • 43 slides

Belief space planning assuming maximum likelihood observations

Belief space planning assuming maximum likelihood observations. Robert Platt Russ Tedrake, Leslie Kaelbling, Tomas Lozano-Perez Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology June 30, 2010. Planning from a manipulation perspective.

499 views • 40 slides

Belief space planning assuming maximum likelihood observations

Belief space planning assuming maximum likelihood observations. Robert Platt Russ Tedrake, Leslie Kaelbling, Tomas Lozano-Perez Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology June 30, 2010. Planning from a manipulation perspective.

475 views • 40 slides

Belief space planning assuming maximum likelihood observations

Belief space planning assuming maximum likelihood observations. Robert Platt Russ Tedrake, Leslie Kaelbling, Tomas Lozano-Perez Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology June 30, 2010. Planning from a manipulation perspective.

502 views • 40 slides

Belief space planning assuming maximum likelihood observations

Belief space planning assuming maximum likelihood observations. Robert Platt Russ Tedrake, Leslie Kaelbling, Tomas Lozano-Perez Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology June 30, 2010. Planning from a manipulation perspective.

400 views • 40 slides

Dr. Itamar Arel College of Engineering Electrical Engineering and Computer Science Department

ECE-517: Reinforcement Learning in Artificial Intelligence Lecture 15: Partially Observable Markov Decision Processes (POMDPs). October 27, 2011. Dr. Itamar Arel College of Engineering Electrical Engineering and Computer Science Department The University of Tennessee Fall 2011. Outline.

449 views • 29 slides

An Introduction to PO-MDP

An Introduction to PO-MDP. Presented by Alp Sarda?. MDP. Components: State Action Transition Reinforcement Problem: choose the action that makes the right tradeoffs between the immediate rewards and the future gains, to yield the best possible solution Solution:

364 views • 27 slides

Reinforcement Learning Dealing with Complexity and Safety in RL

371 views • 25 slides

Value function computed starting from the goal

Value function computed starting from the goal. Policy pi of choosing the best action a given a value function V. Value function. Policy. POMDP is like doing MDP in a belief space.

270 views • 20 slides

State Agnostic Planning Graphs

State Agnostic Planning Graphs. William Cushing Daniel Bryce Arizona State University {william.cushing, dan.bryce}@asu.edu. Special thanks to: Subbarao Kambhampati, David E. Smith, Menkes van den Briel, Romeo Sanchez, J. Benton. p q r 6 7. q t r 5 6. p q r 5 6. r q p 5

303 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

364 views • 14 slides

Research Update

Research Update. Ruijie He Oct 11, 2007. Path-planning for Indoor Quadrotor. Challenges No GPS Requires environmental sensors for state estimation Limited payload No SICK laser, range = 50m Hokuyo laser effective range = 3m

205 views • 12 slides

Introduction to Planning

Introduction to Planning. What is Planning?. Either a sequence of actions that get some model into a goal configuration Rubik's Cube Navigating a city via intersections Robot manipulation objects Also High level: Daily Routine. Planning Vocabulary. State Rubik's Cube: 4.33E19 or 5.19E20

211 views • 11 slides

Gaussian Processes for Fast Policy Optimisation of POMDP-based Dialogue Managers

165 views • 1 slides