CPS 173 Security games

1. CPS 173Security games Vincent Conitzer conitzer@cs.duke.edu

2. Recent deployments in security • Tambe’s TEAMCORE group at USC • Airport security • Where should checkpoints, canine units, etc. be deployed? • Deployed at LAX and another US airport, being evaluated for deployment at all US airports • Federal Air Marshals • Coast Guard • …

3. Security example Terminal A Terminal B action action

4. Security game A B A B

5. Some of the questions raised D S D • Equilibrium selection? • How should we model temporal / information structure? • What structure should utility functions have? • Do our algorithms scale? S

6. Observing the defender’s distribution in security Terminal A Terminal B observe Mo Sa Tu Th Fr We This model is not uncontroversial… [Pita, Jain, Tambe, Ordóñez, Kraus AIJ’10; Korzhyk, Yin, Kiekintveld, Conitzer, Tambe JAIR’11; Korzhyk, Conitzer, Parr AAMAS’11]

7. Other nice properties of commitment to mixed strategies • Agrees w. Nash in zero-sum games • Leader’s payoff at least as good as any Nash eq. or even correlated eq. (von Stengel & Zamir [GEB ‘10]; see also Conitzer & Korzhyk [AAAI ‘11], Letchford, Korzhyk, Conitzer [draft]) ≥ • Noequilibrium selection problem

8. Discussion about appropriateness of leadership model in security applications • Mixed strategy not actually communicated • Observability of mixed strategies? • Imperfect observation? • Does it matter much (close to zero-sum anyway)? • Modeling follower payoffs? • Sensitivity to modeling mistakes • Human players… [Pita et al. 2009]

9. Example security game • 3 airport terminals to defend (A, B, C) • Defender can place checkpoints at 2 of them • Attacker can attack any 1 terminal A B C {A, B} {A, C} {B, C}

10. Security resource allocation games[Kiekintveld, Jain, Tsai, Pita, Ordóñez, Tambe AAMAS’09] • Set of targets T • Set of security resources W available to the defender (leader) • Set of schedules • Resource w can be assigned to one of the schedules in • Attacker (follower) chooses one target to attack • Utilities: if the attacked target is defended, otherwise t1 s1 t3 w 1 t2 s2 t5 w 2 t4 s3

11. Game-theoretic properties of security resource allocation games [Korzhyk, Yin, Kiekintveld, Conitzer, Tambe JAIR’11] For the defender: Stackelberg strategies are also Nash strategies minor assumption needed not true with multiple attacks • Interchangeability property for Nash equilibria (“solvable”) • no equilibrium selection problem • still true with multiple attacks [Korzhyk, Conitzer, Parr IJCAI’11]

12. Compact LP • Cf. ERASER-C algorithm by Kiekintveld et al. [2009] • Separate LP for every possible t* attacked: Defender utility Marginal probability of t* being defended (?) Distributional constraints Attacker optimality Slide 11

13. Counter-example to the compact LP w2 .5 .5 • LP suggests that we can cover every target with probability 1… • … but in fact we can cover at most 3 targets at a time .5 t t w1 t t .5 Slide 12

14. Will the compact LP work for homogeneous resources? • Suppose that every resource can be assigned to any schedule. • We can still find a counter-example for this case: t t .5 .5 .5 .5 t t t t .5 .5 r r r 3 homogeneous resources

15. Birkhoff-von Neumann theorem • Every doubly stochasticn x n matrix can be represented as a convex combination of n x n permutation matrices • Decomposition can be found in polynomial time O(n4.5), and the size is O(n2) [Dulmage and Halperin, 1955] • Can be extended to rectangular doubly substochastic matrices = .1 +.1 +.5 +.3 Slide 14

16. Schedules of size 1 using BvN .7 w 1 t1 .2 .1 t2 .3 w 2 .7 t3 .1 .2 .2 .5

17. Algorithms & complexity[Korzhyk, Conitzer, Parr AAAI’10] NP-hard (SAT) P (BvN theorem) NP-hard P (constraint generation) NP-hard NP-hard (3-COVER) Slide 16

18. Placing checkpoints in a city [Tsai, Yin, Kwak, Kempe, Kiekintveld, Tambe AAAI’10; Jain, Korzhyk, Vaněk, Conitzer, Pěchouček, Tambe AAMAS’11]