  Consider a dynamic task allocation problem, where tasks are unknowingly
distributed over an environment. This paper considers each task comprised of
two sequential subtasks: detection and completion, where each subtask can only
be carried out by a certain type of agent. We address this problem using a
novel nature-inspired approach called "hunter and gatherer". The proposed
method employs two complementary teams of agents: one agile in detecting
(hunters) and another skillful in completing (gatherers) the tasks. To minimize
the collective cost of task accomplishments in a distributed manner, a
game-theoretic solution is introduced to couple agents from complementary
teams. We utilize market-based negotiation models to develop incentive-based
decision-making algorithms relying on innovative notions of "certainty and
uncertainty profit margins". The simulation results demonstrate that employing
two complementary teams of hunters and gatherers can effectually improve the
number of tasks completed by agents compared to conventional methods, while the
collective cost of accomplishments is minimized. In addition, the stability and
efficacy of the proposed solutions are studied using Nash equilibrium analysis
and statistical analysis respectively. It is also numerically shown that the
proposed solutions function fairly, i.e. for each type of agent, the overall
workload is distributed equally.
