Rationalizable Strategies for the Navigator–Target–Missile Game

Harel, Meir; Moshaiov, Amiram; Alkaher, David

doi:10.2514/1.g004875

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…To highlight the applicability of this study to real-life problems, consider the aeronautical MOGs of Alkaher and Moshaiov (2016) and Harel et al (2020). In these MOG studies, which are solved using an analytical approach, the primary objectives involve distances among the considered aeronautical entities.…”

Section: Generalmentioning

confidence: 99%

“…In these MOG studies, which are solved using an analytical approach, the primary objectives involve distances among the considered aeronautical entities. In particular, in addition to the primary MOG distance-based objectives, Harel et al (2020) employed a secondary criterion that is similar to the one used in the current toy problem. It is conceivable that the work in Harel et al (2020) could be revised to also include minimization of the maneuvering control efforts of the players, which is a common criterion in such aeronautical games.…”

Section: Generalmentioning

confidence: 99%

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Solving zero‐sum multi‐objective games with a‐priori secondary criteria

Harel

Eisenstadt-Matalon

Moshaiov

2022

Multi Criteria Decision Anal

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Solving non‐cooperative zero‐sum multi‐objective Games (zsMOGs), under undecided objective preferences results, for each of the players, in a Set of Rationalizable Strategies (SRS) to choose from. First, this paper deals with finding for each of the players a preferred subset of such rationalizable strategies based on a‐priori incorporation of partial preferences of the decision‐makers using secondary criteria. The obtained subset is termed the Set of Preferred Strategies (SPS). Here, a novel archive‐based co‐evolutionary algorithm is suggested to search for the SPS for each of the players. An academic example is suggested to demonstrate and validate the algorithm. It concerns a zsMOG that involves two adversarial planar manipulators. Based on a theorem that is proven here, a theoretic reference SRS is found for each of the players. This reference SRS is applied to find a reference SPS, which is used for validating the algorithm. Next, a comparison study is performed between the proposed archive‐based co‐evolutionary algorithm and an elite‐based version of this algorithm. The results clearly show that the archive‐based algorithm is superior to the elite‐based version, yielding results that correspond well to the theoretic sets.

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Section: Generalmentioning

confidence: 99%

Section: Generalmentioning

confidence: 99%

Solving zero‐sum multi‐objective games with a‐priori secondary criteria

Harel

Eisenstadt-Matalon

Moshaiov

2022

Multi Criteria Decision Anal

Self Cite

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“…Second, artificial intelligence-based approaches are used to solve the multicriteria game. For example, the fuzzy set-based algorithm can calculate PNE solutions by constructing membership functions for fuzzy goals [28,29], and the heuristic algorithm can solve it by using a population evolution strategy [30,31]. However, these algorithms are usually used in problems with low real-time requirements, and their application range is always limited for the problem constraints.…”

Section: Introductionmentioning

confidence: 99%

Multicriteria Game Approach to Air-to-Air Combat Tactical Decisions for Multiple UAVs

Jiang,

Luo,

et al. 2023

J. of Syst. Eng. Electron.

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Air-to-air combat tactical decisions for multiple unmanned aerial vehicles (ACTDMU) are a key decision-making step in beyond visual range combat. Complex influencing factors, strong antagonism and real-time requirements need to be considered in the ACTDMU problem. In this paper, we propose a multicriteria game approach to ACTDMU. This approach consists of a multicriteria game model and a Pareto Nash equilibrium algorithm. In this model, we form the strategy profiles for the integration of air-to-air combat tactics and weapon target assignment strategies by considering the correlation between them, and we design the vector payoff functions based on predominance factors. We propose a algorithm of Pareto Nash equilibrium based on preference relations using threshold constraints (PNE-PRTC), and we prove that the solutions obtained by this algorithm are refinements of Pareto Nash equilibrium solutions. The numerical experiments indicate that PNE-PRTC algorithm is considerably faster than the baseline algorithms and the performance is better. Especially on large-scale instances, the Pareto Nash equilibrium solutions can be calculated by PNE-PRTC algorithm at the second level. The simulation experiments show that the multicriteria game approach is more effective than one-side decision approaches such as multiple-attribute decision-making and randomly chosen decisions.

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“…First, it is difficult to establish a scene model of a multi-member, multi-role game due to the extremely large increase in the dimension of the state quantity; second, it has high requirements for the accuracy of the prior knowledge, and the success rate of the game is low if the prior knowledge of the players in the game cannot be obtained accurately; third, the differential game algorithm is complicated, involving a high-dimensional matrix operation, power function operation, integral calculation, etc., which places a high demand on the computational resources of the spacecraft. More on this topic can be found in [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Guidance Strategy for Active Spacecraft Protection from a Homing Interceptor via Deep Reinforcement Learning

Ni,

Liu,

et al. 2023

Mathematics

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The cooperative active defense guidance problem for a spacecraft with active defense is investigated in this paper. An engagement between a spacecraft, an active defense vehicle, and an interceptor is considered, where the target spacecraft with active defense will attempt to evade the interceptor. Prior knowledge uncertainty and observation noise are taken into account simultaneously, which are vital for traditional guidance strategies such as the differential-game-based guidance method. In this set, we propose an intelligent cooperative active defense (ICAAI) guidance strategy based on deep reinforcement learning. ICAAI effectively coordinates defender and target maneuvers to achieve successful evasion with less prior knowledge and observational noise. Furthermore, we introduce an efficient and stable convergence (ESC) training approach employing reward shaping and curriculum learning to tackle the sparse reward problem in ICAAI training. Numerical experiments are included to demonstrate ICAAI’s real-time performance, convergence, adaptiveness, and robustness through the learning process and Monte Carlo simulations. The learning process showcases improved convergence efficiency with ESC, while simulation results illustrate ICAAI’s enhanced robustness and adaptiveness compared to optimal guidance laws.

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Rationalizable Strategies for the Navigator–Target–Missile Game

Cited by 11 publications

References 13 publications

Solving zero‐sum multi‐objective games with a‐priori secondary criteria

Solving zero‐sum multi‐objective games with a‐priori secondary criteria

Multicriteria Game Approach to Air-to-Air Combat Tactical Decisions for Multiple UAVs

Cooperative Guidance Strategy for Active Spacecraft Protection from a Homing Interceptor via Deep Reinforcement Learning

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