Collaborative multi agent physical search with probabilistic knowledge

This paper considers the setting wherein a group of agents (e.g., robots) is seeking to obtain a given tangible good, potentially available at different locations in a physical environment. Traveling between locations, as well as acquiring the good at any given location consumes from the resources available to the agents (e.g., battery charge). The availability of the good at any given location, as well as the exact cost of acquiring the good at the location is not fully known in advance, and observed only upon physically arriving at the location. However, apriori probabilities on the availability and potential cost are provided. Given such as setting, the problem is to find a strategy/plan that maximizes the probability of acquiring the good while minimizing resource consumption. Sample applications include agents in exploration and patrol missions, e.g., rovers on Mars seeking to mine a specific mineral. Although this model captures many real world scenarios, it has not been investigated so far. We focus on the case where locations are aligned along a path, and study several variants of the problem, analyzing the effects of communication and coordination. For the case that agents can communicate, we present a polynomial algorithm that works for any fixed number of agents. For non-communicating agents, we present a polynomial algorithm that is suitable for any number of agents. Finally, we analyze the difference between homogeneous and heterogeneous agents, both with respect to their allotted resources and with respect to their capabilities.