# CS 188: Artificial Intelligence Spring Announcements

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1 CS 188: Artificial Intelligence Spring 2011 Lecture 18: HMMs and Particle Filtering 4/4/2011 Pieter Abbeel --- UC Berkeley Many slides over this course adapted from Dan Klein, Stuart Russell, Andrew Moore Announcements W4 out, due next week Monday P4 out, due next week 2 1

2 Announcements Course contest Fun! (And extra credit.) Regular tournaments Instructions posted soon! 3 Outline Markov Models ( = a particular Bayes net) Hidden Markov Models (HMMs) Representation ( = another particular Bayes net) Inference Forward algorithm ( = variable elimination) Particle filtering ( = likelihood weighting with some tweaks) Why do we study them? Widespread use for reasoning over time or space Concept: Stationary distribution 5 2

3 Reasoning over Time Often, we want to reason about a sequence of observations Speech recognition Robot localization User attention Medical monitoring Need to introduce time into our models Basic approach: hidden Markov models (HMMs) More general: dynamic Bayes nets 6 Markov Models A Markov model is a chain-structured BN Each node is identically distributed (stationarity) Value of X at a given time is called the state As a BN: X 1 X 2 X 3 X 4 Parameters: called transition probabilities or dynamics, specify how the state evolves over time (also, initial probs) 3

4 Conditional Independence X 1 X 2 X 3 X 4 Basic conditional independence: Past and future independent of the present Each time step only depends on the previous This is called the (first order) Markov property Note that the chain is just a (growing) BN We can always use generic BN reasoning on it if we truncate the chain at a fixed length 8 Example: Markov Chain Weather: States: X = {rain, sun} Transitions: 0.9 Initial distribution: 1.0 sun rain sun What s the probability distribution after one step? sun rain This are two new representations of a CPT, not BNs! sun rain 9 4

5 Query: P(X t ) Question: probability of being in state x at time t? Slow answer: Enumerate all sequences of length t which end in s Add up their probabilities = join on X 1 through X t-1 followed by sum over X 1 through X t-1 10 Mini-Forward Algorithm Question: What s P(X) on some day t? An instance of variable elimination! (In order X 1, X 2, ) sun sun sun sun rain rain rain rain Forward simulation 11 5

6 Example From initial observation of sun P(X 1 ) P(X 2 ) P(X 3 ) P(X ) From initial observation of rain P(X 1 ) P(X 2 ) P(X 3 ) P(X ) 12 Stationary Distributions If we simulate the chain long enough: What happens? Uncertainty accumulates Eventually, we have no idea what the state is! Stationary distributions: For most chains, the distribution we end up in is independent of the initial distribution Called the stationary distribution of the chain Usually, can only predict a short time out Mathematically: P (X) = x P t+1 t (X x)p (x) 6

8 Outline Markov Models ( = a particular Bayes net) Hidden Markov Models (HMMs) Representation ( = another particular Bayes net) Inference Forward algorithm ( = variable elimination) Particle filtering ( = likelihood weighting with some tweaks) 16 Hidden Markov Models Markov chains not so useful for most agents Eventually you don t know anything anymore Need observations to update your beliefs Hidden Markov models (HMMs) Underlying Markov chain over states S You observe outputs (effects) at each time step As a Bayes net: X 1 X 2 X 3 X 4 X 5 E 1 E 2 E 3 E 4 E 5 8

9 Example An HMM is defined by: Initial distribution: Transitions: Emissions: Ghostbusters HMM P(X 1 ) = uniform P(X X ) = usually move clockwise, but sometimes move in a random direction or stay in place P(R ij X) = same sensor model as before: red means close, green means far away. 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 P(X 1 ) 1/6 1/6 1/2 X 1 X 2 X 3 X 4 X 5 R i,j R i,j R i,j R i,j E5 0 1/ P(X X =<1,2>) 9

10 Conditional Independence HMMs have two important independence properties: Markov hidden process, future depends on past via the present Current observation independent of all else given current state X 1 X 2 X 3 X 4 X 5 E 1 E 2 E 3 E 4 E 5 Quiz: does this mean that observations are independent given no evidence? [No, correlated by the hidden state] Real HMM Examples Speech recognition HMMs: Observations are acoustic signals (continuous valued) States are specific positions in specific words (so, tens of thousands) Machine translation HMMs: Observations are words (tens of thousands) States are translation options Robot tracking: Observations are range readings (continuous) States are positions on a map (continuous) 10

11 Filtering / Monitoring Filtering, or monitoring, is the task of tracking the distribution B(X) (the belief state) over time We start with B(X) in an initial setting, usually uniform As time passes, or we get observations, we update B(X) The Kalman filter was invented in the 60 s and first implemented as a method of trajectory estimation for the Apollo program Example: Robot Localization Example from Michael Pfeiffer Prob 0 t=0 Sensor model: can read in which directions there is a wall, never more than 1 mistake Motion model: may not execute action with small prob. 1 11

12 Example: Robot Localization Prob 0 t=1 Lighter grey: was possible to get the reading, but less likely b/c required 1 mistake 1 Example: Robot Localization Prob 0 1 t=2 12

13 Example: Robot Localization Prob 0 1 t=3 Example: Robot Localization Prob 0 1 t=4 13

14 Example: Robot Localization Prob 0 1 t=5 Inference: Base Cases Observation Given: P(X 1 ), P(e 1 X 1 ) Query: P(x 1 e 1 ) 8 x 1 Passage of Time Given: P(X 1 ), P(X 2 X 1 ) Query: P(x 2 ) 8 x 2 X 1 X 1 X 2 E 1 14

15 Passage of Time Assume we have current belief P(X evidence to date) Then, after one time step passes: X 1 X 2 Or, compactly: Basic idea: beliefs get pushed through the transitions With the B notation, we have to be careful about what time step t the belief is about, and what evidence it includes Example: Passage of Time As time passes, uncertainty accumulates T = 1 T = 2 T = 5 Transition model: ghosts usually go clockwise 15

16 Observation Assume we have current belief P(X previous evidence): X 1 Then: E 1 Or: Basic idea: beliefs reweighted by likelihood of evidence Unlike passage of time, we have to renormalize Example: Observation As we get observations, beliefs get reweighted, uncertainty decreases Before observation After observation 16

17 Example HMM The Forward Algorithm We are given evidence at each time and want to know We can derive the following updates We can normalize as we go if we want to have P(x e) at each time step, or just once at the end = exactly variable elimination in order X 1, X 2, 17

18 Online Belief Updates Every time step, we start with current P(X evidence) We update for time: X 1 X 2 We update for evidence: X 2 E 2 The forward algorithm does both at once (and doesn t normalize) Problem: space is X and time is X 2 per time step Outline HMMs: representation HMMs: inference Forward algorithm Particle filtering = sampling-based approximate inference method = likelihood weighting + resampling Why useful? Whenever state-space is too large to run forward algorithm in reasonable amount of time

19 Particle Filtering Filtering: approximate solution Sometimes X is too big to use exact inference X may be too big to even store B(X) E.g. X is continuous Solution: approximate inference Track samples of X, not all values Samples are called particles Time per step is linear in the number of samples But: number needed may be large In memory: list of particles, not states This is how robot localization works in practice Particle is just new name for sample Representation: Particles Our representation of P(X) is now a list of N particles (samples) Generally, N << X Storing map from X to counts would defeat the point P(x) approximated by number of particles with value x So, many x will have P(x) = 0! More particles, more accuracy For now, all particles have a weight of 1 Particles: (3,3) (2,3) (3,3) (3,2) (3,3) (3,2) (2,1) (3,3) (3,3) (2,1) 44 19

20 Particle Filtering: Elapse Time Each particle is moved by sampling its next position from the transition model This is like prior sampling samples frequencies reflect the transition probs Here, most samples move clockwise, but some move in another direction or stay in place This captures the passage of time If we have enough samples, close to the exact values before and after (consistent) Particle Filtering: Observe Slightly trickier: We don t sample the observation, we fix it This is similar to likelihood weighting, so we downweight our samples based on the evidence Note that, as before, the probabilities don t sum to one, since most have been downweighted (in fact they sum to an approximation of P(e)) 20

21 Particle Filtering: Resample Rather than tracking weighted samples, we resample N times, we choose from our weighted sample distribution (i.e. draw with replacement) This is equivalent to renormalizing the distribution Now the update is complete for this time step, continue with the next one Old Particles: (3,3) w=0.1 (2,1) w=0.9 (2,1) w=0.9 (3,1) w=0.4 (3,2) w=0.3 (2,2) w=0.4 (1,1) w=0.4 (3,1) w=0.4 (2,1) w=0.9 (3,2) w=0.3 Old Particles: (2,1) w=1 (2,1) w=1 (2,1) w=1 (3,2) w=1 (2,2) w=1 (2,1) w=1 (1,1) w=1 (3,1) w=1 (2,1) w=1 (1,1) w=1 Robot Localization In robot localization: We know the map, but not the robot s position Observations may be vectors of range finder readings State space and readings are typically continuous (works basically like a very fine grid) and so we cannot store B(X) Particle filtering is a main technique [Demos] Global-floor FastSlam 21

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