  The problem of search by multiple agents to find and localize objects arises
in many important applications. In this paper, we study a class of multi-agent
search problems in which each agent can access only a subset of a discrete
search space, with detection performance that depends only on the location. We
show that this problem can be reformulated as a minimum cost network
optimization problem, and develop a fast specialized algorithm for the
solution. We prove that our algorithm is correct, and has worst case
computation performance that is faster than general minimum cost flow
algorithms. We also address the problem where detection performance depends on
both location and agent, which is known to be NP-Hard. We reduce the problem to
a submodular maximization problem over a matroid, and provide an approximate
algorithm with guaranteed performance. We illustrate the performance of our
algorithms with simulations of search problems and compare it with other
min-cost flow algorithms.
