Adaptation of the Simulated Risk Disambiguation Protocol to a Discrete Setting

1. Adaptation of the Simulated Risk Disambiguation Protocol to a Discrete Setting ICAPS Workshop on POMDP, Classification and Regression: Relationships and Joint Utilization June 8, 2006 Cumbria, England Al Aksakalli, Donniell Fishkind, Carey Priebe Department of Applied Mathematics and Statistics Johns Hopkins University

2. Outline: • Problem Description • MDP and POMDP Formulations • Adaptation of the Simulated Risk Protocol • Computational Experiments

3. Problem Description: Spatial arrangement of detections: true detections , false detections

4. Problem Description: Spatial arrangement of detections: true detections , false detections .29 .11 .72 .26 .61 .23 .39 We only see .59 .72 .89 .68 .83 .13 .64 Assume for all that is the probability that .32 .27

5. Problem Description: Given start and destination .29 .11 .72 .26 .61 .23 .39 .59 start s .72 .89 t destination .68 .83 .13 .64 .32 .27

6. Problem Description: Given start and destination .29 .11 .72 About each detection there is a hazard region , an open disk of fixed radius .26 .61 .23 .39 .59 s .72 .89 t .68 .83 .13 .64 .32 .27

7. Problem Description: Given start and destination .29 .11 .72 About each detection there is a hazard region , an open disk of fixed radius .26 .61 .23 ?? .39 .59 s We seek a continuous curve from to in of shortest achievable arclength .72 ?? .89 t .68 .83 .13 .64 .32 .27

8. Problem Description: Given start and destination .29 .11 .72 About each detection there is a hazard region , an open disk of fixed radius .26 .61 .23 .39 .59 s We seek a continuous curve from to in of shortest achievable arclength .72 .89 t .68 .83 .13 .64 …and we assume the ability to disambiguatedetections from the boundary of their hazard regions. .32 .27

9. Problem Description: Given start and destination .29 .11 .72 About each detection there is a hazard region , an open disk of fixed radius .26 .61 .23 true .39 .59 s We seek a continuous curve from to in of shortest achievable arclength .72 .89 t .68 .83 .13 .64 …and we assume the ability to disambiguate detections from the boundary of their hazard regions. .32 .27

10. Problem Description: Given start and destination .29 .11 .72 About each detection there is a hazard region , an open disk of fixed radius .26 .61 .23 …or false .39 .59 s We seek a continuous curve from to in of shortest achievable arclength .89 t .68 .83 .13 .64 …and we assume the ability to disambiguate detections from the boundary of their hazard regions. .32 .27

11. Problem Description: Given start and destination .29 .11 .72 About each detection there is a hazard region , an open disk of fixed radius .26 .59 s We seek a continuous curve from to in of shortest achievable arclength .89 t .68 .83 .13 .64 …and we assume the ability to disambiguate detections from the boundary of their hazard regions. .32 .27 the rest of the transversal…

12. Definition: A disambiguation protocol is a function # disambiguations allowed cost per disambiguation which detection disambiguated next… …and where the disambiguation performed

13. Example 1: Protocol gives rise to the RDP Length=707.97, prob=.89670 Length=1116.19, prob=.10330

14. Example 2:Protocol gives rise to the RDP (superimposed composite)

15. Random Disambiguation Paths (RDP) Problem: Given , find protocol of minimum .

16. Related work: • Canadian Traveller Problem (CTP): Graph theoretic RDP • Given a finite graph –edges with specific probabilities of being traversable, and a starting and a destination vertex – each edge’s status is revealed only when one of the end points is visited: objective is to minimize expected traversal length • Shown to be #P-hard

17. Markov Decision Process (MDP) formulation: Let be the information vector keeping track of the decision maker’s current knowledge; be the set of all possible disambiguation points RDP Problem can be cast as a K-stage finite horizon MDP with States: Actions: where v is a disambiguation point and i is a hazard region index Rewards: the negative of the shortest path distance between the state vertex and the action vertex minus c, if not going to d - d is an absorbative state for which there is a one-time and very large reward for entering Transitions: governed by ‘s

18. Partially Observable Markov Decision Process (POMDP) formulation: RDP problem can be cast as a POMDP by trimming the information vector to and folding the ambiguity of the hazards into ambiguity of the information vector, hence the partial observability of the state.

19. Risk Simulation Protocol: For purpose of deciding next disambiguation point, we pretend that ambiguous disks are riskily traversable… ? ? ? traversal ? ?

20. Risk Simulation Protocol: For purpose of deciding next disambiguation point, we pretend that ambiguous disks are riskily traversable… ? ? ? traversal ? ? is the usual Euclidean length of . is the surprise length of , which is the negative logarithm of the probability that is traversable in actuality.

21. Given undesirability function (henceforth, monotonically non-decreasing in its arguments) and, say,

22. Given undesirability function (henceforth, monotonically non-decreasing in its arguments) and, say, Definition:The simulated risk protocol is defined as dictating that the next disambiguation be at the first ambiguous point of . ? ? ? traversal ? ?

23. Given undesirability function (henceforth, monotonically non-decreasing in its arguments) and, say, Definition:The simulated risk protocol is defined as dictating that the next disambiguation be at the first ambiguous point of . ? ? ? traversal ? ? How to proceed once this disambiguation is performed: update and , decrement , and set the new s to be y.

24. How to navigate in this continuous setting: The Tangent Arc Graph (TAG) is the superimposition/subdivision of all visibility graphs generated by all subsets of disks. • For any undesirability function, is an path in TAG !

25. Linear undesirability functions: • Because of the efficiency in their realization, we will • consider simulated risk protocols generated by linear undesirability • functions for a chosen parameter . • As a further shorthand, denote such a protocol by .

26. How (during the simulation of risk phase) can be affected by :

27. How (during the simulation of risk phase) can be affected by :

28. How (during the simulation of risk phase) can be affected by :

29. How (during the simulation of risk phase) can be affected by :

30. How (during the simulation of risk phase) can be affected by :

31. Example 1: Protocol gives rise to the RDP Length=707.97, prob=.89670 Length=1116.19, prob=.10330

32. Example 2: Protocol gives rise to the RDP (superimposed composite)

33. A discrete version of RDP (DRDP): Discretization via a subgraph of the integer lattice with unit edge lengths:

34. Adapting the simulated risk protocol to the lattice discretization: Again, consider a linear undesirability function: - u is the Euclidean length, - v is the surprise length ( ) Each edge in G is weighted with where 1 is the indicator function, and comp() is the number of connected components of its argument (Each time a hazard region intersects an edge, half of the surprise length is added to that edge’s weight)

35. Example:(Simulated risk protocol & RDP are computed effortlessly)

36. Computational experiments: A 40 by 20 integer lattice is used Each hazard region is a disk with radius 5.5 Disk centers sampled from a uniform distribution of integers in ‘s sampled from uniform distribution on (0,1) Cost of disambiguation is taken as 1.5 For each N, K combination, 50 different instances were sampled Optimal solutions found by solving the MDP model via value iteration

37. Illustration with N=7, K=1: Expected length:

38. Comparison of optimal versus simulated risk: Runtime to find overall optimal (SR-RDP runtime negligible) • Simulated risk found the optimal solution 74% of the time • Overall mean percentage error of simulated risk solutions was less than 1% • For N=7, K=3; VI took more than an hour • for N=10, K=1; VI did not run due to insufficient memory

39. Q & A