1. Planning Under Uncertainty

2. 573 Core Topics Agency Problem Spaces Search Knowledge Representation Reinforcement Learning InferencePlanning Supervised Learning Logic-Based Probabilistic

3. Administrivia Reading for today’s class: ch 17 thru 17.3 Reading for Thursday: ch 21 Reinforcement learning Problem Set Extension until Monday 11/10 8am One additional problem Tues 11/11 – no class Thurs 11/13 – midterm In class Closed book May bring 1 sheet of 8.5x11” paper

4. Semantics Syntax: a description of the legal arrangements of symbols (Def “sentences”) Semantics: what the arrangement of symbols means in the world Sentences Models Sentences Representation World Semantics Inference

5. Propositional Logic: SEMANTICS “Interpretation” (or “possible world”) Specifically, TRUTH TABLES Assignment to each variable either T or F Assignment of T or F to each connective Think “function mapping from P to T (or F)” P T T F F Q P  Q T FF F Does P  Q |= P  Q ?

6. First Order Logic Syntax more complex Semantics more complex Specifically, the mappings are more complex (And the range of the mappings is more complex)

7. Models Depiction of one possible “real-world” model

8. Interpretations=Mappings syntactic tokens  model elements Depiction of one possible interpretation, assuming Constants: Functions: Relations: Richard JohnLeg(p,l)On(x,y) King(p)

9. Interpretations=Mappings syntactic tokens  model elements Another interpretation, same assumptions Constants: Functions: Relations: Richard JohnLeg(p,l)On(x,y) King(p)

10. Satisfiability, Validity, & Entailment S is valid if it is true in all interpretations S is satisfiable if it is true in some interp S is unsatisfiable if it is false all interps S1 entails S2 if forall interps where S1 is true, S2 is also true |=

11. 11 Simple proof from def of conditional probability Bayes rules! Previously, in 573…

12. © Daniel S. Weld 12 Joint Distribution All you need - Can answer any question Inference by enumeration But… exponential Both time & space Solution: exploit conditional independence

13. © Daniel S. Weld 13 Joint Distribution All you need to know Can answer any question Inference by enumeration But… exponential Both time & space Solution: exploit conditional independence

14. © Daniel S. Weld 14 Sample Bayes Net Earthquake BurglaryAlarm Nbr2CallsNbr1Calls Pr(B=t) Pr(B=f) 0.05 0.95 Pr(A|E,B) e,b 0.9 (0.1) e,b 0.2 (0.8) e,b 0.85 (0.15) e,b 0.01 (0.99) Radio

15. © Daniel S. Weld 15 Given Markov Blanket, X is Independent of All Other Nodes MB(X) = Par(X)  Childs(X)  Par(Childs(X)) Burglary Alarm Sounded Earthquake

16. © Daniel S. Weld 16 Inference in BNs We generally want to compute Pr(X), or Pr(X|E) where E is (conjunctive) evidence The graphical independence representation Efficient inference, organized by network shape Two simple algorithms: Variable elimination (VE) Junction trees Approximate: Markov Chain Sampling

17. © Daniel S. Weld 17 Learning Parameter Estimation: Maximum Likelihood (ML) Maximum A Posteriori (MAP) Bayesian Learning Parameters for a Bayesian Network Learning Structure of Bayesian Networks Naïve Bayes Models Hidden Variables(later)

18. Parameter Estimation Summary PriorHypothesis Maximum Likelihood Estimate Maximum A Posteriori Estimate Bayesian Estimate UniformThe most likely AnyThe most likely Any Weighted combination

19. Parameter Estimation and Bayesian Networks EBRAJM TFTTFT FFFFFT FTFTTT FFFTTT FTFFFF... P(A|E,B) = ? P(A|E,¬B) = ? P(A|¬E,B) = ? P(A|¬E,¬B) = ? Prior + data= Beta(2,3) (3,4)

20. Structure Learning as Search Local Search 1.Start with some network structure 2.Try to make a change (add or delete or reverse edge) 3.See if the new network is any better What should the initial state be? Uniform prior over random networks? Based on prior knowledge? Empty network? How do we evaluate networks? © Daniel S. Weld 20

21. Naïve Bayes F 2F N-2F N-1F NF 1F 3 Class Value … Assume that features are conditionally ind. given class variable Works well in practice Forces probabilities towards 0 and 1

22. Tree Augmented Naïve Bayes (TAN) [Friedman,Geiger & Goldszmidt 1997] F 2F N-2F N-1F NF 1F 3 Class Value … Models limited set of dependencies Guaranteed to find best structure Runs in polynomial time

23. Naïve Bayes for Text P(spam | w 1 … w n ) Independence assumption? Spam? appledictatorNigeria …

24. 24 Naïve Bayes for Text Modeled as generating a bag of words for a document in a given category by repeatedly sampling with replacement from a vocabulary V = {w 1, w 2,…w m } based on the probabilities P(w j | c i ). Smooth probability estimates with Laplace m-estimates assuming a uniform distribution over all words (p = 1/|V|) and m = |V| Equivalent to a virtual sample of seeing each word in each category exactly once.

25. 25 Text Naïve Bayes Algorithm (Train) Let V be the vocabulary of all words in the documents in D For each category c i  C Let D i be the subset of documents in D in category c i P(c i ) = |D i | / |D| Let T i be the concatenation of all the documents in D i Let n i be the total number of word occurrences in T i For each word w j  V Let n ij be the number of occurrences of w j in T i Let P(w i | c i ) = (n ij + 1) / (n i + |V|)

26. 26 Text Naïve Bayes Algorithm (Test) Given a test document X Let n be the number of word occurrences in X Return the category: where a i is the word occurring the ith position in X

27. 27 Naïve Bayes Time Complexity Training Time: O(|D|L d + |C||V|)) where L d is the average length of a document in D. Assumes V and all D i, n i, and n ij pre-computed in O(|D|L d ) time during one pass through all of the data. Generally just O(|D|L d ) since usually |C||V| < |D|L d Test Time: O(|C| L t ) where L t is the average length of a test document. Very efficient overall, linearly proportional to the time needed to just read in all the data.

28. 28 Easy to Implement But… If you do… it probably won’t work…

29. Probabilities: Important Detail! Any more potential problems here? P(spam | E 1 … E n ) =  P(spam | E i ) i  We are multiplying lots of small numbers Danger of underflow!  0.5 57 = 7 E -18  Solution? Use logs and add!  p 1 * p 2 = e log(p1)+log(p2)  Always keep in log form

30. 30 Naïve Bayes Posterior Probabilities Classification results of naïve Bayes I.e. the class with maximum posterior probability… Usually fairly accurate (?!?!?) However, due to the inadequacy of the conditional independence assumption… Actual posterior-probability estimates not accurate. Output probabilities generally very close to 0 or 1.

31. 573 Core Topics Agency Problem Spaces Search Knowledge Representation Reinforcement Learning InferencePlanning Supervised Learning Logic-Based Probabilistic

32. Planning Percepts Actions What action next? Static Fully Observable Stochastic Instantaneous Full Perfect Planning under uncertainty Environment

33. Models of Planning ClassicalContingent MDP ???Contingent POMDP ???Conformant POMDP Complete Observation Partial None Uncertainty Deterministic Disjunctive Probabilistic

34. Awkward Never any defn of what an MDP is! Missed prob strips defn Should have done that before the 2DBN All the asumptions about a Markov model could be simplified and eliminated – took too long!

35. Defn: Markov Model Q: set of states  init prob distribution A: transition probability distribution

36. E.g. Predict Web Behavior Q: set of states (Pages)  init prob distribution (Likelihood of site entry point) A: transition probability distribution (User navigation model) When will visitor leave site?

37. E.g. Predict Robot’s Behavior Q: set of states  init prob distribution A: transition probability distribution Will it attack Dieter?

38. Probability Distribution, A Forward Causality The probability of s t does not depend directly on values of future states. Probability of new state could depend on The history of states visited. Pr(s t |s t-1,s t-2,…, s 0 ) Markovian Assumption Pr(s t |s t-1,s t-2,…s 0 ) = Pr(s t |s t-1 ) Stationary Model Assumption Pr(s t |s t-1 ) = Pr(s k |s k-1 ) for all k.

39. What is the cost of representing a stationary probability distribution? Can we do something better?

40. Representing A Q: set of states  init prob distribution A: transition probabilities how can we represent these? s0s0 s1s1 s2s2 s3s3 s4s4 s5s5 s6s6 s 0 s 1 s 2 s 3 s 4 s 5 s 6 s0s1s2s3s4s5s6s0s1s2s3s4s5s6 p 12 Probability of transitioning from s 1 to s 2 ∑ ?

41. Factoring Q Represent Q simply as a set of states? Is there internal structure? Consider a robot domain What is the state space? s0s0 s1s1 s2s2 s3s3 s4s4 s5s5 s6s6

42. A Factored domain Six Boolean Variables : has_user_coffee (huc), has_robot_coffee (hrc), robot_is_wet (w), has_robot_umbrella (u), raining (r), robot_in_office (o) How many states? 2 6 = 64

43. Representing  Compactly Q: set of states  init prob distribution How represent this efficiently? s0s0 s1s1 s2s2 s3s3 s4s4 s5s5 s6s6 r u hrc w With a Bayes net (of course!)

44. Representing A Compactly Q: set of states  init prob distribution A: transition probabilities s0s0 s1s1 s2s2 s3s3 s4s4 s5s5 s6s6 s 0 s 1 s 2 s 3 s 4 s 5 s 6 … s 35 s 0 s 1 s 2 s 3 s 4 s 5 s 6 … s 35 p 12 How big is matrix version of A?4096

45. 2-Dynamic Bayesian Network huc hrc w u r o o r u w huc 8 4 16 4 2 2 Total values required to represent transition probability table = 36 Vs. 4096 required for a complete state probablity table? T T+1

46. Dynamic Bayesian Network huc hrc w u r o o r u w huc T T+1 Also known as a Factored Markov Model Defined formally as * Set of random vars * BN for initial state * 2-layer DBN for transitions huc hrc w u r o 0

47. STRIPS Action Schemata (:operator pick-up :parameters ((block ?ob1)) :precondition (and (clear ?ob1) (on-table ?ob1) (arm-empty)) :effect (and (not (clear ?ob1)) (not (on-table ?ob1)) (not (arm-empty)) (holding ?ob1))) Instead of defining ground actions: pickup-A and pickup-B and … Define a schema: Note: strips doesn’t allow derived effects; you must be complete! }

48. Deterministic “STRIPS”? pick_up_A arm_empty clear_A on_table_A - arm_empty - on_table_A + holding_A

49. Probabilistic STRIPS? pick_up_A arm_empty clear_A on_table_A P<0.9 - arm_empty - on_table_A + holding_A

50. Observability Full Observability Partial Observability No Observability

51. Reward/cost Each action has an associated cost. Agent may accrue rewards at different stages. A reward may depend on The current state The (current state, action) pair The (current state, action, next state) triplet Additivity assumption : Costs and rewards are additive. Reward accumulated = R(s 0 )+R(s 1 )+R(s 2 )+…

52. Horizon Finite : Plan till t stages. Reward = R(s 0 )+R(s 1 )+R(s 2 )+…+R(s t ) Infinite : The agent never dies. The reward R(s 0 )+R(s 1 )+R(s 2 )+… Could be unbounded. Discounted reward : R(s 0 )+γR(s 1 )+ γ 2 R(s 2 )+… Average reward : lim n  ∞ (1/n)[Σ i R(s i )] ?

53. Goal for an MDP Find a policy which: maximizes expected discounted reward over an infinite horizon for a fully observable Markov decision process. Why shouldn’t the planner find a plan?? What is a policy??

54. Optimal value of a state Define V*(s) `value of a state’ as the maximum expected discounted reward achievable from this state. Value of state if we force it to do action “a” right now, but let it act optimally later: Q*(a,s)=R(s) + c(a) + γΣ s’εS Pr(s’|a,s)V*(s’) V* should satisfy the following equation: V*(s) = max aεA {Q*(a,s)} = R(s) + max aεA {c(a) + γΣ s’εS Pr(s’|a,s)V*(s’)}

55. Value iteration Assign an arbitrary assignment of values to each state (or use an admissible heuristic). Iterate over the set of states and in each iteration improve the value function as follows: V t+1 (s)=R(s) + max aεA {c(a)+γΣ s’εS Pr(s’|a,s) V t (s’)} `Bellman Backup’ Stop the iteration appropriately. V t approaches V* as t increases.

56. Max Bellman Backup a1a1 a2a2 a3a3 s VnVn VnVn VnVn VnVn VnVn VnVn VnVn Q n+1 (s,a) V n+1 (s)

57. Stopping Condition ε-convergence : A value function is ε –optimal if the error (residue) at every state is less than ε. Residue(s)=|V t+1 (s)- V t (s)| Stop when max sεS R(s) < ε

58. Complexity of value iteration One iteration takes O(|S| 2 |A|) time. Number of iterations required : poly(|S|,|A|,1/(1-γ)) Overall, the algo is polynomial in state space! Thus exponential in number of state vars.

59. Computation of optimal policy Given the value function V*(s), for each state, do Bellman backups and the action which maximises the inner product term is the optimal action.  Optimal policy is stationary (time independent) – intuitive for infinite horizon case.

60. Policy evaluation Given a policy Π:S  A, find value of each state using this policy. V Π (s) = R(s) + c(Π(s)) + γ[Σ s’εS Pr(s’| Π(s),s)V Π (s’)] This is a system of linear equations involving |S| variables.

61. Bellman’s principle of optimality A policy Π is optimal if V Π (s) ≥ V Π’ (s) for all policies Π’ and all states s є S. Rather than finding the optimal value function, we can try and find the optimal policy directly, by doing a policy space search.

62. Policy iteration Start with any policy (Π 0 ). Iterate Policy evaluation : For each state find V Π i (s). Policy improvement : For each state s, find action a* that maximises Q Π i (a,s). If Q Π i (a*,s) > V Π i (s) let Π i+1 (s) = a* else let Π i+1 (s) = Π i (s) Stop when Π i+1 = Π i Converges faster than value iteration but policy evaluation step is more expensive.

63. Modified Policy iteration Rather than evaluating the actual value of policy by solving system of equations, approximate it by using value iteration with fixed policy.

64. RTDP iteration Start with initial belief and initialize value of each belief as the heuristic value. For current belief Save the action that minimises the current state value in the current policy. Update the value of the belief through Bellman Backup. Apply the minimum action and then randomly pick an observation. Go to next belief assuming that observation. Repeat until goal is achieved.

65. Fast RTDP convergence What are the advantages of RTDP? What are the disadvantages of RTDP? How to speed up RTDP?

66. Other speedups Heuristics Aggregations Reachability Analysis

67. Going beyond full observability In execution phase, we are uncertain where we are, but we have some idea of where we can be. A belief state = ?

68. Models of Planning ClassicalContingent MDP ???Contingent POMDP ???Conformant POMDP Complete Observation Partial None Uncertainty Deterministic Disjunctive Probabilistic

69. Speedups Reachability Analysis More informed heuristic

70. Mathematical modelling Search space : finite/infinite state/belief space. Belief state = some idea of where we are Initial state/belief. Actions Action transitions (state to state / belief to belief) Action costs Feedback : Zero/Partial/Total

71. Algorithms for search A* : works for sequential solutions. AO* : works for acyclic solutions. LAO* : works for cyclic solutions. RTDP : works for cyclic solutions.

72. Full Observability Modelled as MDPs. (also called fully observable MDPs) Output : Policy (State  Action) Bellman Equation V*(s)=max aεA(s) [c(a)+Σ s’εS V*(s’)P(s’|s,a)]

73. Partial Observability Modelled as POMDPs. (partially observable MDPs). Also called Probabilistic Contingent Planning. Belief = probabilistic distribution over states. What is the size of belief space? Output : Policy (Discretized Belief -> Action) Bellman Equation V*(b)=max aεA(b) [c(a)+Σ oεO P(b,a,o) V*(b a o )]

74. No observability Deterministic search in the belief space. Output ?