A practical algorithm was obtained for directly generating an optimal guessing sequence for guessing under lies. An optimal guessing strategy was defined as one which minimizes the number of average number of guesses in determining the correct value of a random variable. The information-theoretic bounds on the average number of guesses for optimal strategies were also derived

Arikan, E.

Boztaş, S.

Bilkent University Institutional Repository

ISIT 2002, Lausanne, Switzerland, June 30 -July 5,2002 Guessing with Lies Erdal Arikan' Serdar Boztag Electrical-Electronics Engineering Dept. Dept. of Mathematics, RMIT University Bilkent University, 06533 Ankara, Turkey e-mail: arikanQee . bilkent  .edu. tr I. INTRODUCTION The noiseless case ([l, 31) of the guessing problem is when a sequence of questions of the form Is X = a:? are posed until a YES answer determines the correct value of a random variable X with range X = { 2 1 , 2 2 , .  . .} and distribution PX (see also [2, 41 for extensions). Here, we assume there is a nonzero probability that the NO answer received is not the right answer, while the YES answer is noiseless. Let L denote the number of lies (erroneous NO answers) encountered during the course of the search. L may depend on X but is independent of the algorithm employed to  find X .  Let Px,L(z, e) be the joint distribution of ( X ,  L) .  A guessing strategy for identifying X is any sequence 91, g2,. . . of ele- ments from X-gi will be the i th probe if all previous probes have yielded NO answers. An optimal guessing strategy is one which minimizes the average number of guesses e=o z~~ where G(z , l )  is the time index of the (a  + 1)th probe of x. Clearly, the guessing functions G satisfy the precedence con- straints G(z,  k + I) > G(z, k )  for all z and k > 0. It can be shown that this problem is equivalent to a noiseless guessing problem on the ( X , L )  space with no false answers but sub- ject to  precedence constraints. Such a problem is very difficult to solve explicitly. We obtain (i) a practical algorithm for di- rectly generating an optimal guessing sequence for guessing X under lies L;  (ii) information-theoretic bounds on the average number of guesses for optimal strategies. 11. THE OPTIMAL GUESSING ALGORITHM Our algorithm generates an optimal guessing sequence one probe at  a time. At any point, each element z E X will have been probed kx times. The state vector ( k ,  : x E X )  indicates that the algorithm has probed the set of points {(.,e) : z E X ,  0 5 e < k z }  and received NO answers. Given the current state ( k z  : z E X ) ,  the next probe has to be chosen from the available set { (z, k,); z E X } .  for any fixed x, a simple greedy algorithm that probes the element ( 2 ,  I C z )  in the avail- able set for which P(z,k , )  is largest is optimal. Otherwise, the simple greedy algorithm may fail to be optimal. The op- timal algorithm in the general case uses a different metric to prioritize its search. Define for e 2  2 e, 2 0 If P(z ,e )  is nonincreasing in 'E. Arikan was visiting the RMIT Mathematics Department, supported by an RMIT Faculty of Applied Science grant, when this work was in part performed. Melbourne 3001, Australia e-mail: serdarQrmit . edu. au We call an algorithm a greedy-A algorithm if it chooses its next probe from the available set so as to maximize the quantity Ax(kx). Theorem 1 A n y  guessing sequence generated in accordance with the greedy-A algorithm i s  optimal,  i.e., it at tains  the  min- imum possible average number of guesses. Greedy-A algorithms typically generate their guesses in batches; i.e., they probe the same element successively a num- ber of times before moving on to another element. This prop- erty is used to  bound the expectation E[G] : Theorem 2 Let  G be any  optimal guessing func t ion  f o r  guessing X in the presence of lies L .  T h e n ,  t he  average num- ber of guesses f o r  G i s  upperbounded by E[G] I 1 + e x ~ [ H i p ( Q ) ]  E[GI 2 (1 + lnIXI)-' exp[Hi/z(Q)] (2) and lowerbounded by (3) where Q is  a distribution derived f r o m  the  batches of G ,  and Hlp(Q)  = In( xi a )2 i s  the Re'nyi entropy of order $. Remark: Assume Px,L(z,~) is nonincreasing in e > 0 for each fixed z E X ;  e.g., a geometric distribution with an x-dependent parameter. Then, each batch has size 1, and the bounds of Theorem 2 are valid with PX,L in place of Q. The R6nyi entropy of order 1/2 satisfies H ~ I ~ ( P x , L )  = HI/S(PLIX) + H1/2(Px), where the conditional R6nyi entropy is defined as H 1 p ( P ~ , x )  = In E, [Ee d-1'. In this case, the guessing effort can be thought of as consisting of two parts, one directed at X ,  the other at L given X .  We also note that the A-greedy algorithm can be modified to  efficiently solve the general noiseless guessing problem with precedence constraints. This problem can, in principle, be solved by Markov decision theory and dynamic programming at the cost of exponential complexity in search domain size. REFERENCES [l] E. Arikan, An Inequality on Guessing and Its Application to Sequential Decoding, IEEE Trans. Inform. Theory, Vol. IT-42, pp. 99-105, 1996. [2] E. Arikan and N. Merhav, Joint Source-channel Coding and Guessing with Application to Sequential Decoding, IEEE Trans. Inform. Theory, Vol. IT-44, pp. 1756-1769, 1998. [3] S. Boztq, Comments on 'An Inequality on Guessing and Its Application to Sequential Decoding', IEEE Trans. Inform. The- ory, Vol. 43, No. 6, pp. 2062-2063, November 1997. [4] N. Merhav and E. Arikan, The Shannon Cipher System with a Guessing Wiretapper, IEEE Trans. Inform. Theory, Vol. IT-45, pp. 1860-1866, 1999. 208 0-7803-7501 -7/02/$17.00@2002 IEEE. 

Guessing with lies

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Guessing with lies

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