Metareasoning refers to reasoning about one's own decision making process. This paper considers metareasoning about the decision making process in multi-agent settings. We present a multiagent metareasoning approach that enables a multi-agent team to select which task allocation algorithm to use as a function of changing communication quality level. Given a set of multi-agent task allocation algorithms, we synthesize a policy that prescribes the best algorithm to use among a predefined set of algorithms for a given communication level. Since each agent in the team runs the same policy, the team (or a part of the team) will collectively switch between task allocation algorithms as a function of the observed level of communication. We apply reactive synthesis to generate the policy from high-level specifications written in Linear Temporal Logic encoding the agents' switching behavior with respect to the state of the environment. We perform experiments in simulation to identify the best performing algorithms under different communication levels. The communication environment is modeled using the Rayleigh fading model and communication estimation is done through the exchange of heartbeat messages among agents. We test our metareasoning policy in three types of scenarios: search & rescue, fire monitoring, and ship protection scenarios. For each scenario, we demonstrate that our policy achieved better performance with respect to either max distance traveled, max number of transmitted messages or both compared to running any single algorithm.
Bidirectional path and motion planning approaches decrease planning time, on average, compared to their unidirectional counterparts. In the context of single-query feasible motion planning, using bidirectional search to find a continuous motion plan requires an explicit connection between the forward search tree and the reverse search tree. Such a tree-tree connection requires solving a two-point Boundary Value Problem (BVP). However, two-point BVP solution can be difficult or impossible to calculate for many types of vehicles (using numerical methods to find a solution, such as shooting approaches may be computationally expensive and is sometimes numerically unstable). To overcome this challenge, we present a generalized bidirectional search algorithm that does not require solving two-point BVP. Instead of connecting the two trees directly, our algorithm uses the cost information of the reverse tree as a guiding heuristic for forward search. This enables the forward search to quickly converge to a full feasible solution without an explicit tree-tree connection and without the solution to a two-point BVP. We run multiple software simulations in different environments and using dynamics of different vehicles along with real-world hardware experiments to show that our approach performs very close or better than existing state of the art approaches in terms of quickly converging to an initial feasible solution.
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