CS 541: Artificial Intelligence

1. CS 541: Artificial Intelligence Lecture VIII: Temporal Probability Models

2. Re-cap of Lecture VII • Exact inference by variable elimination: • polytime on polytrees, NP-hard on general graphs • space = time, very sensitive to topology • Approximate inference by LW, MCMC: • LW does poorly when there is lots of (downstream) evidence • LW, MCMC generally insensitive to topology • Convergence can be very slow with probabilities close to 1 or 0 • Can handle arbitrary combinations of discrete and continuous variables

3. Re-cap of Lecture IX • Machine Learning • Classification (Naïve Bayes) • Decision trees • Regression (Linear, Smoothing) • Linear Separation (Perceptron, SVMs) • Non-parametric classification (KNN)

4. Outline • Time and uncertainty • Inference: filtering, prediction, smoothing • Hidden Markov models • Kalman filters (a brief mention) • Dynamic Bayesian networks • Particle filtering

5. Time and uncertainty

6. Time and uncertainty • The world changes: we need to track and predict it • Diabetes management vs vehiclediagnosis • Basic idea: copy state and evidence variables for each time step • = set of unobservable state variables at time t • E.g., BloodSugart, StomachContentst, etc. • = set of observable evidence variables at time t • E.g., MeasuredBloodSugart, PulseRatet, FoodEatent • This assumes discrete time; step size depends on problem • Notation:

7. Markov processes (Markov chain) • Construct a Bayesian net from these variables: parents? • Markov assumption: depends on bounded set of • First order Markov process: • Second order Markov process: • Sensor Markov assumption: • Stationary process: transition model and sensor model are fixed for all t

8. Example • First-order Markov assumption not exactly true in real world! • Possible fixes: • 1. Increase order of Markov process • 2. Augment state, e.g., add Tempt, Pressuret • Example: robot motion. • Augment position and velocity with Batteryt

9. Inference

10. Inference tasks • Filtering: P(Xt|e1:t) • Belief state--input to the decision process of a rational agent • Prediction: P(Xt+k|e1:t) for k > 0 • Evaluation of possible action sequences; • Like filtering without the evidence • Smoothing: P(Xk|e1:t) for 0 ≤ k < t • Better estimate of past states, essential for learning • Most likely explanation: • Speech recognition, decoding with a noisy channel

11. Filtering • Aim: devise a recursive state estimation algorithm • I.e., prediction + estimation. Prediction by summing out Xt: • where • Time and space constant ( independent of t )

12. Filtering example

13. Smoothing • Divide evidence e1:t into e1:k, ek+1:t : • Backward message computed by a backwards recursion:

14. Smoothing example • Forward-backward algorithm: cache forward messages along the way • Time linear in t (polytree inference), space O(t|f|)

15. Most likely explanation • Most likely sequence ≠ sequence of most likely states!!!! • Most likely path to each xt+1 • =most likely path to some xt plus one more step • Identical to filtering, except f1:t replaced by • I.e., m1:i(t) gives the probability of the most likely path to state i • Update has sum replaced by max, giving the Viterbi algorithm:

16. Viterbi algorithm

17. Hidden Markov models

18. Hidden Markov models • Xtis a single, discrete variable (usually Et is too) • Domain of Xt is {1; ...; S} • Transition matrix Tij = P(Xt =j|Xt-1 =i) e.g., • Sensor matrix Ot for each time step, diagonal elements P(et|Xt =i) • e.g., with U1 =true, • Forward and backward messages as column vectors: • Forward-backward algorithm needs time O(S2t) and space O(St)

19. Country dance algorithm • Can avoid storing all forward messages in smoothing by running forward algorithm backwards: • Algorithm: forward pass computes ft, backward pass does fi, bi

20. Country dance algorithm • Can avoid storing all forward messages in smoothing by running forward algorithm backwards: • Algorithm: forward pass computes ft, backward pass does fi, bi

21. Country dance algorithm • Can avoid storing all forward messages in smoothing by running forward algorithm backwards: • Algorithm: forward pass computes ft, backward pass does fi, bi

22. Country dance algorithm • Can avoid storing all forward messages in smoothing by running forward algorithm backwards: • Algorithm: forward pass computes ft, backward pass does fi, bi

23. Country dance algorithm • Can avoid storing all forward messages in smoothing by running forward algorithm backwards: • Algorithm: forward pass computes ft, backward pass does fi, bi

24. Country dance algorithm • Can avoid storing all forward messages in smoothing by running forward algorithm backwards: • Algorithm: forward pass computes ft, backward pass does fi, bi

25. Country dance algorithm • Can avoid storing all forward messages in smoothing by running forward algorithm backwards: • Algorithm: forward pass computes ft, backward pass does fi, bi

26. Country dance algorithm • Can avoid storing all forward messages in smoothing by running forward algorithm backwards: • Algorithm: forward pass computes ft, backward pass does fi, bi

27. Country dance algorithm • Can avoid storing all forward messages in smoothing by running forward algorithm backwards: • Algorithm: forward pass computes ft, backward pass does fi, bi

28. Country dance algorithm • Can avoid storing all forward messages in smoothing by running forward algorithm backwards: • Algorithm: forward pass computes ft, backward pass does fi, bi

29. Kalman filtering

30. Inference by stochastic simulation • Modelling systems described by a set of continuous variables, • E.g., tracking a bird flying: • Airplanes, robots, ecosystems, economies, chemical plants, planets, … • Gaussian prior, linear Gaussian transition model and sensor model

31. Updating Gaussian distributions • Prediction step: if P(Xt|e1:t) is Gaussian, then prediction is Gaussian. If P(Xt+1|e1:t) is Gaussian, then the updated distribution is Gaussian • HenceP(Xt|e1:t)is multi-variateGaussian for all t • General (nonlinear, non-Gaussian) process: description of posterior grows unboundedly as

32. Simple 1-D example • Gaussian random walk on X-axis, s.d. , sensor s.d.

33. General Kalman update • Transition and sensor models • F is the matrix for the transition; the transition noise covariance • H is the matrix for the sensors; the sensor noise covariance • Filter computes the following update: • where is the Kalman gain matrix • and are independent of observation sequence, so compute offline

34. 2-D tracking example: filtering

35. 2-D tracking example: smoothing

36. Where is breaks • Cannot be applied if the transition model is nonlinear • Extended Kalman Filter models transition as locally linear around • Fails if systems is locally unsmooth

37. Dynamic Bayesian networks

38. Dynamic Bayesian networks • Xt, Et contain arbitrarily many variables in a replicated Bayes net

39. DBNs vs. HMMs • Every HMM is a single-variable DBN; every discrete DBN is an HMM • Sparse dependencies: exponentially fewer parameters; • e.g., 20 state variables, three parents each • DBN has parameters, HMM has

40. DBN vs. Kalman filters • Every Kalman filter model is a DBN, but few DBNs are KFs; • Real world requires non-Gaussian posteriors • E.g., where are bin Laden and my keys? What's the battery charge?

41. Exact inference in DBNs • Naive method: unroll the network and run any exact algorithm • Problem: inference cost for each update grows with t • Rollup filtering: add slice t+1, “sum out”slice t using variable elimination • Largest factor is O(dn+1), update cost O(dn+2) (cf. HMM update cost O(d2n))

42. Likelihood weighting for DBN • Set of weighted samples approximates the belief state • LW samples pay no attention to the evidence! • fraction “agreeing” falls exponentially with t • Number of samples required grows exponentially with t

43. Particle filtering

44. Particle filtering • Basic idea: ensure that the population of samples ("particles") tracks the high-likelihood regions of the state-space • Replicate particles proportional to likelihood for et • Widely used for tracking non-linear systems, esp, vision • Also used for simultaneous localization and mapping in mobile robots, 105-dimensional state space

45. Particle filtering • Assume consistent at time t: • Propagate forward: populations of xt+1 are • Weight samples by their likelihood for et+1 : • Resample to obtain populations proportional to W :

46. Particle filtering performance • Approximation error of particle filtering remains bounded over time, at least empirically theoretical analysis is difficult

47. How particle filter works & applications

48. Summary • Temporal models use state and sensor variables replicated over time • Markov assumptions and stationarity assumption, so we need • Transition model P(Xt|Xt-1) • Sensor model P(Et|Xt) • Tasks are filtering, prediction, smoothing, most likely sequence; • All done recursively with constant cost per time step • Hidden Markov models have a single discrete state variable; • Widely used for speech recognition • Kalman filters allow n state variables, linear Gaussian, O(n3) update • Dynamic Bayesian nets subsume HMMs, Kalman filters; exact update intractable • Particle filtering is a good approximate filtering algorithm for DBNs