Path Planning Using Probability Tensor Flows
This work addresses path planning for agents in simulated environments, but it is incremental as it extends an existing model to include non-absorbing obstacles, multiple goals, and multiple agents.
The paper tackled the problem of modeling agent motion in complex environments with goals and obstacles by applying probability propagation, resulting in realistic behaviors and always finding feasible solutions in simulated grids.
Probability models have been proposed in the literature to account for "intelligent" behavior in many contexts. In this paper, probability propagation is applied to model agent's motion in potentially complex scenarios that include goals and obstacles. The backward flow provides precious background information to the agent's behavior, viz., inferences coming from the future determine the agent's actions. Probability tensors are layered in time in both directions in a manner similar to convolutional neural networks. The discussion is carried out with reference to a set of simulated grids where, despite the apparent task complexity, a solution, if feasible, is always found. The original model proposed by Attias has been extended to include non-absorbing obstacles, multiple goals and multiple agents. The emerging behaviors are very realistic and demonstrate great potentials of the application of this framework to real environments.