Improving the Scalability of the MagnitudeBased Deceptive PathPlanning Using Subgoal Graphs Deceptive pathplanning is the task of finding a path so as to minimize the probability of an observer (or a defender) identifying the observed agentxe2x80x99s final goal before the goal has been reached. Magnitudebased deceptive pathplanning takes advantage of the quantified deceptive values upon each grid or position to generate paths that are deceptive. Existing methods using optimization techniques cannot satisfy the time constraints when facing with the largescale terrain, as its computation time grows exponentially with the size of road maps or networks. In this work, building on recent developments in the optimal path planner, the paper proposes a hybrid solution between map scaling and hierarchical abstractions. By leading the path deception information down into a general purpose but highlyefficient pathplanning formulation, the paper substantially speeds up the task upon large scale terrains with an admissible loss of deception.

Single Real Goal, MagnitudeBased Deceptive PathPlanning Deceptive pathplanning is the task of finding a path so as to minimize the probability of an observer (or a defender) identifying the observed agentxe2x80x99s final goal before the goal has been reached. It is one of the important approaches to solving realworld challenges, such as public security, strategic transportation, and logistics. Existing methods either cannot make full use of the entire environmentsxe2x80x99 information, or lack enough flexibility for balancing the pathxe2x80x99s deceptivity and available moving resource. In this work, building on recent developments in probabilistic goal recognition, we formalized a single real goal magnitudebased deceptive pathplanning problem followed by a mixedinteger 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 pathplanning compared to the existing one.