Randomized path planning with deceptive strategies

Root, Philip J; Mot, Jan De; Féron, Éric

doi:10.1109/acc.2005.1470188

Cited by 8 publications

(16 citation statements)

References 2 publications

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“…Figure 3 illustrates an example of threats located along a subset of the edges of the graph of feasible flight paths. In the figure, three formations are located on three different nodes (7,13,19) in the graph, and the edges connect the starting points to the end point, which is the strategic target location. Transition probabilities p a,b express the survivability of the blue team along edge (a, b).…”

Section: Hierarchical Decision Makingmentioning

confidence: 99%

“…Learning update mechanisms such as fictitious play are considered in reference [11], whereas techniques pertaining to risk management and stochastic programming are investigated in reference [12]. Other game-based approaches are used to derive optimal policies in the context of opposing UAV teams, where availability of information and anticipation of the strategies of the opponents play a crucial role [13,14]. In the context of air operations, stochastic dynamic programming and risk-averse control techniques are proposed in reference [15].…”

Section: Prior Workmentioning

confidence: 99%

“…Nevertheless, the computational load is an increasing function of the number of formations and vehicles per formation, the path length, the number of threats between consecutive targets, and the number of targets [3]. D 2 VUAS requires implementing the decision vector expressed in equation (13) or (24) whose computational load is low. The computational load is likely to increase with the number of vehicles, although it is difficult to assess precisely since D 2 VUAS is specifically designed for 2-by-n and 3-by-n missions.…”

Section: Practical Considerations: Failures and Computational Loadmentioning

confidence: 99%

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A hierarchical decision and information system for multi-aircraft combat missions

Léchevin

Rabbath

2010

Proceedings of the Institution of Mechanical Engineers, Part G:

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Designing decision, control and information systems is motivated, in part, by the need to support the deployment of multiple aircraft, such as combat vehicles, unmanned combat air vehicles, unmanned aerial vehicles, and weapons, in missions taking place in a dynamic, although uncertain, environment. Such systems aim at ensuring mission success without overloading the operating crew, the pilots, and the commanders. One of the main design challenges lies in obtaining some sort of coherent behaviour of the fleet, by means of solutions to potentially NPhard problems, given incomplete and imperfect information, and despite limited computational and communication capabilities. In this context, this article proposes a hierarchical decision and information system aiming at providing, in real-time, coordinated aircraft path planning and deceptive engagement assignments. The blue-red engagement policy is obtained by minimizing, and balancing, the energy expenditure among the vehicles while constraining information exchanges to a minimum defined by a risk of inconsistency. The proposed system relies on dynamic programming, online heuristic techniques and stochastic, consistency-checking methods. Numerical simulations show that the proposed approach compares advantageously to a random process and to a law that seeks to minimize the cost of the confrontation at a given time regardless of past moves. However, there is a trade-off between increasing the level of deception and the level of energy consumption.Results from stochastic approximation algorithms are used in the sequel [16]. As previously discussed, J o depends onÛ j,o , which is a known matrix, whereas recursions J 1,o andJ 1,o depend on the nominal matrix U * f ,o , where f ∈ {i, j, k} indicates the vehicle that will become the leader for the next 3 × 3 engagement, i.e.

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Section: Hierarchical Decision Makingmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Section: Practical Considerations: Failures and Computational Loadmentioning

confidence: 99%

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A hierarchical decision and information system for multi-aircraft combat missions

Léchevin

Rabbath

2010

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Deceptive planning in an adversarial environment enables humans or AI agents to cover their real intentions or mislead the opponent's situation awareness. This would be of great help to many real-world applications like deceptive network intrusion [1], robotic soccer competition [2], intelligence reconnaissance [3], real-time strategy games, privacy protection [4], important convoy escorting, strategic transportation, or even military operations. Deceptive path-planning is one of its representative tasks.…”

Section: Introductionmentioning

confidence: 99%

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

Zeng

et al. 2020

Entropy

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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. Magnitude-based deceptive path-planning 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 large-scale 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 highly-efficient path-planning formulation, the paper substantially speeds up the task upon large scale terrains with an admissible loss of deception.

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“…Dynamic solutions are thus proposed by Bell (2004), Gentry and Feron (2004), and Root et al (2005). These methods are attractive, but do depend upon high-network connectivity to achieve good results.…”

Section: Introductionmentioning

confidence: 99%

Ambush avoidance in vehicle routing for valuable delivery

2010

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In this work we consider route planning for valuable delivery in an urban environment under the threat of ambushes in which a vehicle, starting from a depot, has to serve a set of predetermined destinations during the day. We provide a method to plan for hardly predictable multi-destination routing extending a minmax flow-based model available for single-destination cases. We then formulate the process of selecting a visiting order as a game to obtain a mixed routing strategy. We analyse the application of the method to a set of simulation scenarios and compare the mixed routing strategy against the best routing. Finally we develop further the methodology introducing a second-level optimization model which reduces the overall risk associated to the proposed mixed routing strategy.2

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Randomized path planning with deceptive strategies

Cited by 8 publications

References 2 publications

A hierarchical decision and information system for multi-aircraft combat missions

A hierarchical decision and information system for multi-aircraft combat missions

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

Ambush avoidance in vehicle routing for valuable delivery

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