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In this paper we extend the one-on-one stochastic duel model with searching to the many-on-one case based on information-sharing. We have derived the probability density function of the time to kill the target in many-on-one model. It is illustrated by an example in which the firing time and the searching time are of different exponential distributions.

The stochastic duel problem has been studied extensively in the past. Ancker [

Friedman [

Wand et al. [

In this paper we extend the one-on-one stochastic duel model with searching given in [

The remainder of this paper is organized as follows. The notation is described in Section 2. Then, in Section 3, the probability model is developed. In Section 4, this model is restricted to the negative exponential distribution case to allow ease of computation. Finally, we draw some concluding remarks and a brief discussion of some possible extensions in Section 5.

Suppose that the two sides in the duel are A and B, respectively. There are k combatants on side A which are. They are hidden defenders, and their mission is to kill the invaders or the attackers. Side B only has one hidden attacker.

Two sides, A and B, conduct a duel satisfying the following assumptions:

1) All the information about B is shared among A’s members. When one of them detects B, every one will know.

2) Each combatant fires simultaneously until B is killed after B being detected.

3) Every member of side A has the same fixed probability, p, of killing B.

4) A’s firing time (that is, the time between rounds) is a random variable with a known probability density function,. And each firing time is selected from, independently and at random.

5) Side B is a passive target.

Other notations which we will use are as follows.

: The time for A to detect B. It is a positive random variable.

: The probability density function (pdf) of.

: A’s firing time. The pdf of is (see above 4)). It is a positive random variable too.

: The characteristic function of.

: The time for A to kill B, measured from the beginning of A’s searching B.

: The firing round times of A until killing B.

: The pdf of.

: The multiple convolution of the density function. That is,

Consider the pdf of random variable

The probability, , that A takes between time t and to kill B in the condition of is the probability of a kill on the first round times the probability side A took between time t and to fire the first round, plus the probability of a kill on the second round times the probability side A took between time t and to fire the two rounds and so on; thus

where

where. Since A’s firing time, , density function is, the time at which the nth round is fired is the sum of n independent selections from, so we have

When (3) and (4) are substituted into (2), we have

or

Before continuing we shall give the characteristic function of firing time distribution. The characteristic function of is

The following properties of are readily demonstrated.

Let and. Then (5) can be rewritten as

The characteristic function for the conditional density function (or) is denoted by From the convolution property of characteristic functions, (9) may be transformed into

From (8), (10) and, we have

The inversion of is as follows:

then

From (11), we can derive the probability density function of,.

When (11) is substituted into (12), we have

Suppose that the probability density functions of and are of exponentials with parameters and, respectively, then

and

where,. The characteristic function of is

From (13), we obtain that

Applying the residue theorem, we have

It is readily demonstrated that has the following properties:

1)2)3).

Proof of property 3): When, we have

when, the same result can be derived.

In this article we extend the one-on-one stochastic duel model with searching to the many-on-one case based on information sharing. The probability density function of the time to kill the target is obtained in integral form. And it is illustrated by an example where the firing time and the searching time are of different exponential distributions.