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Hidden Markov Model (HMM) Sensor Markov assumption: P ( E t | X 0: t , E 1: t 1 ) = P ( E t | X t ) Stationary process: transition model P ( X t | X t 1 ) and Hidden Markov Models sensor model P ( E t | X t ) fixed for all t HMM is a

  1. Hidden Markov Model (HMM) Sensor Markov assumption: P ( E t | X 0: t , E 1: t − 1 ) = P ( E t | X t ) Stationary process: transition model P ( X t | X t − 1 ) and Hidden Markov Models sensor model P ( E t | X t ) fixed for all t HMM is a special type of Bayes net, X t is single discrete random variable: AIMA Chapter 15, Sections 1–5 with joint probability distribution P ( X 0: t , E 1: t ) = P ( X 0 ) Π t i =1 P ( X i | X i − 1 ) P ( E i | X i ) AIMA Chapter 15, Sections 1–5 1 AIMA Chapter 15, Sections 1–5 4 Time and uncertainty Example Consider a target tracking problem R t −1 P(R ) t t 0.7 f 0.3 X t = set of unobservable state variables at time t Rain t −1 Rain Rain t +1 t e.g., Position t , Appearance t , etc. R P(U ) t t E t = set of observable evidence variables at time t t 0.9 f 0.2 e.g., Imagepixels t Umbrella t −1 Umbrella Umbrella t +1 t This assumes discrete time ; step size depends on problem First-order Markov assumption not exactly true in real world! Notation: X a : b = X a , X a +1 , . . . , X b − 1 , X b Possible fixes: 1. Increase order of Markov process 2. Augment state , e.g., add Temp t , Pressure t Example: robot motion. Augment position and velocity with Battery t AIMA Chapter 15, Sections 1–5 2 AIMA Chapter 15, Sections 1–5 5 Markov processes (Markov chains) Inference tasks Filtering: P ( X t | e 1: t ) Construct a Bayes net from these variables: belief state—input to the decision process of a rational agent Markov assumption: X t depends on bounded subset of X 0: t − 1 Prediction: P ( X t + k | e 1: t ) for k > 0 First-order Markov process: P ( X t | X 0: t − 1 ) = P ( X t | X t − 1 ) evaluation of possible action sequences; Second-order Markov process: P ( X t | X 0: t − 1 ) = P ( X t | X t − 2 , X t − 1 ) like filtering without the evidence X t −2 X t −1 X t X t +1 X t +2 Smoothing: P ( X k | e 1: t ) for 0 ≤ k < t First−order better estimate of past states, essential for learning Most likely explanation: arg max x 1: t P ( x 1: t | e 1: t ) speech recognition, decoding with a noisy channel X t −2 X t −1 X t X t +1 X t +2 Second−order Stationary process: transition model P ( X t | X t − 1 ) fixed for all t AIMA Chapter 15, Sections 1–5 3 AIMA Chapter 15, Sections 1–5 6

  2. Filtering Filtering Aim: devise a recursive state estimation algorithm: Aim: devise a recursive state estimation algorithm: P ( X t +1 | e 1: t +1 ) = f ( e t +1 , P ( X t | e 1: t )) P ( X t +1 | e 1: t +1 ) = f ( e t +1 , P ( X t | e 1: t )) P ( X t +1 | e 1: t +1 ) = P ( X t +1 | e 1: t , e t +1 ) = α P ( e t +1 | X t +1 , e 1: t ) P ( X t +1 | e 1: t ) = α P ( e t +1 | X t +1 ) P ( X t +1 | e 1: t ) I.e., prediction + estimation. Prediction by summing out X t : P ( X t +1 | e 1: t +1 ) = α P ( e t +1 | X t +1 ) Σ x t P ( X t +1 , x t | e 1: t ) = α P ( e t +1 | X t +1 ) Σ x t P ( X t +1 | x t , e 1: t ) P ( x t | e 1: t ) = α P ( e t +1 | X t +1 ) Σ x t P ( X t +1 | x t ) P ( x t | e 1: t ) f 1: t +1 = Forward ( f 1: t , e t +1 ) where f 1: t = P ( X t | e 1: t ) Time and space constant (independent of t ) AIMA Chapter 15, Sections 1–5 7 AIMA Chapter 15, Sections 1–5 10 Filtering Filtering example Aim: devise a recursive state estimation algorithm: 0.500 0.627 0.500 0.373 P ( X t +1 | e 1: t +1 ) = f ( e t +1 , P ( X t | e 1: t )) True 0.500 0.818 0.883 False 0.500 0.182 0.117 Rain 0 Rain 1 Rain 2 P ( X t +1 | e 1: t +1 ) = P ( X t +1 | e 1: t , e t +1 ) = α P ( e t +1 | X t +1 , e 1: t ) P ( X t +1 | e 1: t ) = α P ( e t +1 | X t +1 ) P ( X t +1 | e 1: t ) Umbrella 1 Umbrella 2 P ( X t +1 | e 1: t +1 ) = α P ( e t +1 | X t +1 ) Σ x t P ( X t +1 | x t ) P ( x t | e 1: t ) P ( R t ) P ( U t ) R t − 1 R t t 0.7 t 0.9 f 0.3 f 0.2 AIMA Chapter 15, Sections 1–5 8 AIMA Chapter 15, Sections 1–5 11 Filtering Smoothing X 0 X 1 X k X t Aim: devise a recursive state estimation algorithm: P ( X t +1 | e 1: t +1 ) = f ( e t +1 , P ( X t | e 1: t )) E E k E t 1 Divide evidence e 1: t into e 1: k , e k +1: t : P ( X t +1 | e 1: t +1 ) = P ( X t +1 | e 1: t , e t +1 ) P ( X k | e 1: t ) = P ( X k | e 1: k , e k +1: t ) = α P ( e t +1 | X t +1 , e 1: t ) P ( X t +1 | e 1: t ) = α P ( X k | e 1: k ) P ( e k +1: t | X k , e 1: k ) = α P ( e t +1 | X t +1 ) P ( X t +1 | e 1: t ) = α P ( X k | e 1: k ) P ( e k +1: t | X k ) I.e., prediction + estimation. Prediction by summing out X t : = α f 1: k b k +1: t P ( X t +1 | e 1: t +1 ) = α P ( e t +1 | X t +1 ) Σ x t P ( X t +1 , x t | e 1: t ) = α P ( e t +1 | X t +1 ) Σ x t P ( X t +1 | x t , e 1: t ) P ( x t | e 1: t ) = α P ( e t +1 | X t +1 ) Σ x t P ( X t +1 | x t ) P ( x t | e 1: t ) AIMA Chapter 15, Sections 1–5 9 AIMA Chapter 15, Sections 1–5 12

  3. Smoothing Most likely explanation X 0 X 1 X k X t Most likely sequence � = sequence of most likely states!!!! Most likely path to each x t +1 E E k E t 1 = most likely path to some x t plus one more step Divide evidence e 1: t into e 1: k , e k +1: t : x 1 ... x t P ( x 1 , . . . , x t , X t +1 | e 1: t +1 ) max P ( X k | e 1: t ) = P ( X k | e 1: k , e k +1: t )   = P ( e t +1 | X t +1 ) max  P ( X t +1 | x t ) max x 1 ... x t − 1 P ( x 1 , . . . , x t − 1 , x t | e 1: t )  x t = α P ( X k | e 1: k ) P ( e k +1: t | X k , e 1: k ) Identical to filtering, except f 1: t replaced by = α P ( X k | e 1: k ) P ( e k +1: t | X k ) = α f 1: k b k +1: t m 1: t = x 1 ... x t − 1 P ( x 1 , . . . , x t − 1 , X t | e 1: t ) , max Backward message computed by a backwards recursion: I.e., m 1: t ( i ) gives the probability of the most likely path to state i . P ( e k +1: t | X k ) = Σ x k +1 P ( e k +1: t | X k , x k +1 ) P ( x k +1 | X k ) Update has sum replaced by max, giving the Viterbi algorithm: = Σ x k +1 P ( e k +1: t | x k +1 ) P ( x k +1 | X k ) m 1: t +1 = P ( e t +1 | X t +1 ) max x t ( P ( X t +1 | x t ) m 1: t ) = Σ x k +1 P ( e k +1 | x k +1 ) P ( e k +2: t | x k +1 ) P ( x k +1 | X k ) AIMA Chapter 15, Sections 1–5 13 AIMA Chapter 15, Sections 1–5 16 Smoothing example Viterbi example 0.500 0.627 0.500 0.373 Rain 1 Rain 2 Rain 3 Rain 4 Rain 5 True 0.500 0.818 0.883 forward False 0.500 0.182 0.117 true true true true true state space 0.883 0.883 smoothed paths 0.117 0.117 false false false false false true true false true true 0.690 1.000 umbrella backward 0.410 1.000 .8182 .5155 .0361 .0334 .0210 Rain 0 Rain 1 Rain 2 most likely paths .1818 .0491 .1237 .0173 .0024 m 1:1 m 1:2 m 1:3 m 1:4 m 1:5 Umbrella 1 Umbrella 2 Forward–backward algorithm: cache forward messages along the way Time linear in t (polytree inference), space O ( t | f | ) AIMA Chapter 15, Sections 1–5 14 AIMA Chapter 15, Sections 1–5 17 Most likely explanation Example Umbrella Problems Filtering: P ( X t +1 | e 1: t +1 ) = α P ( e t +1 | X t +1 ) Σ x t P ( X t +1 | x t ) P ( x t | e 1: t ) =: f 1: t +1 Smoothing: P ( X k | e 1: t ) = α f 1: k b k +1: t P ( e k +1: t | X k ) = Σ x k +1 P ( e k +1 | x k +1 ) P ( e k +2: t | x k +1 ) P ( x k +1 | X k ) =: b k +1: t R t − 1 P ( R t ) R t P ( U t ) t 0.7 t 0.9 f 0.3 f 0.2 P ( R 3 |¬ u 1 , u 2 , ¬ u 3 ) = ? arg max R 1:3 P ( R 1:3 |¬ u 1 , u 2 , ¬ u 3 ) = ? P ( R 2 |¬ u 1 , u 2 , ¬ u 3 ) = ? AIMA Chapter 15, Sections 1–5 15 AIMA Chapter 15, Sections 1–5 18

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