  We present a multi-agent system where agents can cooperate to solve a system
of dependent tasks, with agents having the capability to explore a solution
space, make inferences, as well as query for information under a limited
budget. Re-exploration of the solution space takes place by an agent when an
older solution expires and is thus able to adapt to dynamic changes in the
environment. We investigate the effects of task dependencies, with
highly-dependent graph $G_{40}$ (a well-known program graph that contains $40$
highly interlinked nodes, each representing a task) and less-dependent graphs
$G_{18}$ (a program graph that contains $18$ tasks with fewer links),
increasing the speed of the agents and the complexity of the problem space and
the query budgets available to agents. Specifically, we evaluate trade-offs
between the agent's speed and query budget. During the experiments, we observed
that increasing the speed of a single agent improves the system performance to
a certain point only, and increasing the number of faster agents may not
improve the system performance due to task dependencies. Favoring faster agents
during budget allocation enhances the system performance, in line with the
"Matthew effect." We also observe that allocating more budget to a faster agent
gives better performance for a less-dependent system, but increasing the number
of faster agents gives a better performance for a highly-dependent system.
