Single Real Goal, Magnitude-Based Deceptive Path-Planning

Abstract

Deceptive path-planning is the task of finding a path so as to minimize the probability of an observer (or a defender) identifying the observed agent’s final goal before the goal has been reached. It is one of the important approaches to solving real-world challenges, such as public security, strategic transportation, and logistics. Existing methods either cannot make full use of the entire environments’ information, or lack enough flexibility for balancing the path’s deceptivity and available moving resource. In this work, building on recent developments in probabilistic goal recognition, we formalized a single real goal magnitude-based deceptive path-planning problem followed by a mixed-integer programming based deceptive path maximization and generation method. The model helps to establish a computable foundation for any further imposition of different deception concepts or strategies, and broadens its applicability in many scenarios. Experimental results showed the effectiveness of our methods in deceptive path-planning compared to the existing one.

Improving the Scalability of the Magnitude-Based Deceptive Path-Planning Using Subgoal Graphs

“…We begin by presenting the problem definition along with the formulation of magnitude-based deceptive path-planning. According to [5,7], the evader has different deception strategies in planning the path. Currently, known strategies include Simulation, Dissimulation, and a weighted Combination.…”

“…This means that deceptive magnitude at each node could be given different values according to different strategies. In the work [7], magnitude-based deceptive path planning model is given as follows. R is the total amount of distance allowed for the deceiver traversing s − g r path.…”

“…Methods are compared by metrics including generation time, path costs and their deceptivity, as shown in Table 1. Same as [7], the computation of the SRGMDM is formulated into a mixed-integer programming and solved using the solvers of CPLEX 11.5 and YALMIP toolbox of MATLAB [43].…”

“…Xu et al [7] formalize a single real goal magnitude-based deceptive path-planning problem followed by a mixed-integer programming-based deceptive path maximization and generation method. With the deception being defined upon each separate node or step, a deceptive path could be easily generated when maximizing deception along the path from the start to the end.…”

“…The computation load during their deceptive path generation [7] mainly centers on two parts: the deceptive magnitude evaluation at each node as well as a global deception maximization. By precomputing the magnitude value, the runtime of identifying the path with maximized deception would only be constrained by the size of the road map or network.…”