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Team, Game, and Negotiation based UAV Task Allocation 69
their security strategies. In the case of multiple cooperative strategies, since
all players communicate with each other during the decision process, they can
decide to adopt a strategy which is beneficial to the overall team goal. One
can also devise some protocol to automate this selection so that communica-
tion between agents can be dispensed with. But when multiple solutions occur
for pure or mixed strategy Nash equilibrium, the agents have to select one of
them. Since every agent can evaluate the search effectiveness function of all the
other agents, they can jointly select a solution whose joint payoff is maximum.
The selection of solution does not involve any communication with the other
agents, but uses the available data through evaluation of search effectiveness
functions. The solution method of choosing a strategy that would maximize
the agents benefit is common for all the agents. When a mixed strategy equi-
librium exists then agents can make a choice based on maximum likelihood
or by random number generation. Here, we choose the maximum likelihood
method.

5.3 Simulation Results
For the purpose of simulation, a region composed of hexagonal grids of size
30×30 is considered. We consider five agents with randomly located initial
positions in the search space. We initially assume a perfect information case
where each agent has the same uncertainty map throughout the search oper-
ation, although it is not a necessary condition. A typical uncertainty map is
shown in Figure 8 along with the initial positions of the searchers. The per-
centage of uncertainty in a cell is proportional to the size of the grey area in
the cell. The total uncertainty in the search space is defined as the sum of the
uncertainties in all the cells.
The uncertainty map is updated at every search step in time. The simula-
tion is carried out for look ahead step lengths of q =1 and q = 2. The agents’
uncertainty reduction factors are assumed to remain constant throughout the

Fig. 8. A typical uncertainty map for 30×30 hexagonal grid