Evasion Game Research Articles

A Region-Based Relay Pursuit Scheme for a Pursuit–Evasion Game With Curvature-Bound Players

A Region-Based Relay Pursuit Scheme for a Pursuit–Evasion Game With Curvature-Bound Players

Evade Unknown Pursuer via Pursuit Strategy Identification and Model Reference Policy Adaptation (MRPA) Algorithm

The game of pursuit–evasion has always been a popular research subject in the field of Unmanned Aerial Vehicles (UAVs). Current evasion decision making based on reinforcement learning is generally trained only for specific pursuers, and it has limited performance for evading unknown pursuers and exhibits poor generalizability. To enhance the ability of an evasion policy learned by reinforcement learning (RL) to evade unknown pursuers, this paper proposes a pursuit UAV attitude estimation and pursuit strategy identification method and a Model Reference Policy Adaptation (MRPA) algorithm. Firstly, this paper constructs a Markov decision model for the pursuit–evasion game of UAVs that includes the pursuer’s attitude and trains an evasion policy for a specific pursuit strategy using the Soft Actor–Critic (SAC) algorithm. Secondly, this paper establishes a novel relative motion model of UAVs in pursuit–evasion games under the assumption that proportional guidance is used as the pursuit strategy, based on which the pursuit UAV attitude estimation and pursuit strategy identification algorithm is proposed to provide adequate information for decision making and policy adaptation. Furthermore, a Model Reference Policy Adaptation (MRPA) algorithm is presented to improve the generalizability of the evasion policy trained by RL in certain environments. Finally, various numerical simulations imply the precision of pursuit UAV attitude estimation and the accuracy of pursuit strategy identification. Also, the ablation experiment verifies that the MRPA algorithm can effectively enhance the performance of the evasion policy to deal with unknown pursuers.

Effectiveness of ex ante honesty oaths in reducing dishonesty depends on content.

Dishonest behaviours such as tax evasion impose significant societal costs. Ex ante honesty oaths-commitments to honesty before action-have been proposed as interventions to counteract dishonest behaviour, but the heterogeneity in findings across operationalizations calls their effectiveness into question. We tested 21 honesty oaths (including a baseline oath)-proposed, evaluated and selected by 44 expert researchers-and a no-oath condition in a megastudy involving 21,506 UK and US participants from Prolific.com who played an incentivized tax evasion game online. Of the 21 interventions, 10 significantly improved tax compliance by 4.5 to 8.5 percentage points, with the most successful nearly halving tax evasion. Limited evidence for moderators was found. Experts and laypeople failed to predict the most effective interventions, though experts' predictions were more accurate. In conclusion, honesty oaths were effective in curbing dishonesty, but their effectiveness varied depending on content. These findings can help design impactful interventions to curb dishonesty.

A Novel Method for a Pursuit–Evasion Game Based on Fuzzy Q-Learning and Model-Predictive Control

This paper explores a pursuit–evasion game (PEG) based on quadrotors by combining fuzzy Q-learning (FQL) and model-predictive control (MPC) algorithms. Initially, the FQL algorithm is employed to perceive, make decisions, and predict the trajectory of the evader. Based on the position and velocity information of both players in the game, the pursuer quadrotor determines its action strategy using the FQL algorithm. Subsequently, a state feedback controller is designed using the MPC algorithm, with reference inputs derived from the FQL algorithm. Within each MPC cycle, the FQL algorithm dynamically provides reference inputs to the MPC, thereby enhancing its robust control and dynamic optimization for the quadrotor. Finally, simulation results verify the effectiveness of the proposed algorithm.

Luring cooperative capture guidance strategy for the pursuit—evasion game under incomplete target information

Luring cooperative capture guidance strategy for the pursuit—evasion game under incomplete target information

Resilient pursuit evasion guidance with feedback game strategy

This paper proposed a resilient guidance strategy for planar pursuit evasion, in which the homing guidance problem is formulated as a Stackelberg game. As is known, the most salient challenge for pursuit evasion guidance is how to ensure an interception despite both players (i.e., missile and target) actively optimize their own interest with preferably incomplete information. Toward this, we introduce a game-theoretic guidance strategy that effectively integrates incentive feedback strategy into pursuit evasion game. In particular, the proposed resilient guidance law takes the form of Stackelberg game conjecture with missile/interceptor as the designated leader, and rigorously proves that leader's interest can be best served with a properly designed feedback gain, and an interception can be made possible in spite of incomplete knowledge on target's intentions (i.e., performance index). Simulation results verify the performance of the proposed strategy.

Maneuver Planning for Multiple Pursuit Intelligent Surface Vehicles in a Sequence of Zero-Sum Pursuit–Evasion Games

Unmanned surface pursuit is a complex and challenging engineering problem, especially when conducted by multiple intelligent surface vehicles (ISVs). To enhance the pursuit performance and facilitate strategic interaction during the target pursuit, this paper proposes a novel game theory-based maneuver planning method for pursuit ISVs. Firstly, a specific two-player zero-sum pursuit–evasion game (ZSPEG)-based target-pursuit model is formed. To ensure the vehicles reach a quick consensus, a target-guided relay-pursuit mechanism and the corresponding pursuit payoffs are designed. Meanwhile, under the fictitious play framework, the behavioral pattern and the strategies of the target could be fictitiously learned. Furthermore, mixed-strategy Nash equilibrium (MNE) is employed to determine the motions for the vehicles, the value of which is the best response in the proposed ZSPEG model. Finally, simulations verify the effectiveness of the above methods in multi-ISV surface pursuit.

A Deep Reinforcement Learning-Based Intelligent Maneuvering Strategy for the High-Speed UAV Pursuit-Evasion Game

Given the rapid advancements in kinetic pursuit technology, this paper introduces an innovative maneuvering strategy, denoted as LSRC-TD3, which integrates line-of-sight (LOS) angle rate correction with deep reinforcement learning (DRL) for high-speed unmanned aerial vehicle (UAV) pursuit–evasion (PE) game scenarios, with the aim of effectively evading high-speed and high-dynamic pursuers. In the challenging situations of the game, where both speed and maximum available overload are at a disadvantage, the playing field of UAVs is severely compressed, and the difficulty of evasion is significantly increased, placing higher demands on the strategy and timing of maneuvering to change orbit. While considering evasion, trajectory constraint, and energy consumption, we formulated the reward function by combining “terminal” and “process” rewards, as well as “strong” and “weak” incentive guidance to reduce pre-exploration difficulty and accelerate convergence of the game network. Additionally, this paper presents a correction factor for LOS angle rate into the double-delay deterministic gradient strategy (TD3), thereby enhancing the sensitivity of high-speed UAVs to changes in LOS rate, as well as the accuracy of evasion timing, which improves the effectiveness and adaptive capability of the intelligent maneuvering strategy. The Monte Carlo simulation results demonstrate that the proposed method achieves a high level of evasion performance—integrating energy optimization with the requisite miss distance for high-speed UAVs—and accomplishes efficient evasion under highly challenging PE game scenarios.

Deep Reinforcement Learning-Based Differential Game Guidance Law against Maneuvering Evaders

To achieve the intelligent interception of different types of maneuvering evaders, based on deep reinforcement learning, a novel intelligent differential game guidance law is proposed in the continuous action domain. Different from traditional guidance laws, the proposed guidance law can avoid tedious manual settings and save cost efforts. First, the interception problem is transformed into the pursuit–evasion game problem, which is solved by zero-sum differential game theory. Next, the Nash equilibrium strategy is obtained through the Markov game process. To implement the proposed intelligent differential game guidance law, an actor–critic neural network based on deep deterministic policy gradient is constructed to calculate the saddle point of the differential game guidance problem. Then, a reward function is designed, which includes the tradeoffs among guidance accuracy, energy consumption, and interception time. Finally, compared with traditional methods, the interception accuracy of the proposed intelligent differential game guidance law is 99.2%, energy consumption is reduced by 47%, and simulation time is shortened by 1.58 s. All results reveal that the proposed intelligent differential game guidance law has better intelligent decision-making ability.

Stability and Motion Patterns of Two Interactive Oscillating Agents

This paper investigates the stability and motion of two interactive oscillating agents. Multiple agents can be controlled in a centralized and/or distributed manner to form specific patterns in cooperative tracking, pursuit, and evasion games, as well as environmental exploration. This paper studies the behavior of two oscillating agents due to their interaction. It shows that, through a combination of selecting oscillation centers and interaction gain, a variety of motions, including limit-cycles and stationary behavior, can be realized.

Orbital Impulsive Pursuit–Evasion Game Formulation and Stackelberg Equilibrium Solutions

The spacecraft pursuit–evasion game in orbit with impulsive maneuvers is investigated based on dynamic game theory. First, the game models are developed progressively from the single-impulse game to the multi-impulse game. In the models, the two-sided optimal strategies of both players form a Stackelberg equilibrium in single-impulse games and constitute a multistage Stackelberg equilibrium in multi-impulse games. Then, the optimality necessary conditions for the two equilibriums are derived and simplified as two multipoint boundary value problems (MPBVPs). To handle the complicated MPBVPs, a rolling backward induction based on the Lambert algorithm accompanied by a homotopy shooting method is proposed to solve the equilibrium solutions. Simulations showed the effectiveness of the proposed methods and verified that the obtained solutions are the (multistage) Stackelberg equilibrium. Meanwhile, the differences between the Stackelberg equilibrium and the Nash equilibrium are illustrated in detail. Finally, an analysis of the influence of the observation delay on the game results is provided.

Multi-Pursuer and One-Evader Evasion Differential Game with Integral Constraints for an Infinite System of Binary Differential Equations

In the Hilbert space l2, a differential evasion game involving multiple pursuers is considered. Integral constraints are imposed on player control functions. The pursuers are tasked with bringing the state of a system back to the origin of l2, while the evader simultaneously tries to avoid it. It is assumed that the energy of the evader is greater than the total energy of the pursuers. In this paper, we contribute to the solution of the differential evasion game with multiple pursuers by building an exact strategy for the evader.

You don’t need an invoice, do you? An online experiment on collaborative tax evasion

Collaborative evasion of taxes and social security fees is prevalent when two parties can coordinate to circumvent third-party reporting mechanisms. Prominent examples are household services when a household hires a service provider and no third party is involved; however, the evidence on the determinants of collaborative tax evasion is scarce. This study examines two coordination mechanisms of collaborative tax evasion: a partner’s signaled intention and information about the majority’s evasion behavior (empirical evasion expectation). We implement an interactive online tax evasion game with 560 participants from an online labor market. Our findings show that manipulating an empirical evasion expectation increases the fraction of evaded transactions by 19 percentage points. Our treatment manipulation of intention signals does not significantly affect evasion; however, when the willingness to evade is signaled first in the chat, the probability of evasion increases by 44 percentage points.

Team formation through an assessor: choosing MARL agents in pursuit–evasion games

Team formation in multi-agent systems usually assumes the capabilities of each team member are known, and the best formation can be derived from that information. As AI agents become more sophisticated, this characterisation is becoming more elusive and less predictive about the performance of a team in cooperative or competitive situations. In this paper, we introduce a general and flexible way of anticipating the outcome of a game for any lineups (the agents, sociality regimes and any other hyperparameters for the team). To this purpose, we simply train an assessor using an appropriate team representation and standard machine learning techniques. We illustrate how we can interrogate the assessor to find the best formations in a pursuit–evasion game for several scenarios: offline team formation, where teams have to be decided before the game and not changed afterwards, and online team formation, where teams can see the lineups of the other teams and can be changed at any time.

Predicting compliance: Leveraging chat data for supervised classification in experimental research

Behavioral and experimental economics have conventionally employed text data to facilitate the interpretation of decision-making processes. This paper introduces a novel methodology, leveraging text data for predictive analytics rather than mere explanation. We detail a supervised classification framework that interprets patterns in chat text to estimate the likelihood of associated numerical outcomes. Despite the unique advantages of experimental data in correlating textual and numerical information for predictive modeling, challenges such as limited sample sizes and potential data skewness persist. To address these, we propose a comprehensive methodological framework aimed at optimizing predictive modeling configurations, particularly in small experimental behavioral research datasets. We also present behavioral experimental data from a preregistered tax evasion game (n=324), demonstrating that chat behavior is not influenced by experimenter demand effects. This establishes chat text as an unbiased variable, enhancing its validity for prediction. Our findings further indicate that beliefs about others’ dishonesty, lying attitudes, and risk preferences significantly impact compliance decisions.

Meta-analyses in Economic Psychology: A sustainable approach to cross-cultural differences

This manuscript is a methodological work on the state of research using meta-analytic procedures in Economic Psychology, with a focus on the investigation of cross-cultural differences. We review published meta-analyses and introduce a new classification thereof by data source, describing how the different categories relate to the study of cross-cultural differences. We also discuss related opportunities and challenges, proposing a sustainable methodological approach that is then implemented in three case studies where we re-analyze data from published meta-analyses. In doing so, the relevance of culture as a determinant is explored by relating country-level cultural indicators to experimental measures of risk aversion, tax compliance, and prosocial behavior, respectively. It turns out that, after we control for country-level cultural heterogeneity and economic development, country-level individualism predicts these economic outcomes. We discuss possible interpretations of our findings. meta-analysis, individualism, fractionalization, Multiple Price List, Tax Evasion Game, Dictator Game

Game Tree Search-based Impulsive Orbital Pursuit–Evasion Game with Limited Actions

This paper focuses on the impulsive orbital pursuit–evasion game (OPEG) with limited action sets for the pursuer and evader. Initially, a mathematical model is developed by combining game theory and orbital dynamics, forming a finite-round impulsive OPEG problem. The problem is then formulated as a bilateral optimization problem, employing a minimum-maximum optimization index based on terminal distance. To tackle this problem, an algorithm based on game tree search is designed, enabling the determination of the optimal pursuit–evasion strategy with limited action sets. Additionally, we explore the influence of the initial pursuing orientation on OPEG. The optimal initial pursuit orientation is analytically derived using relative motion dynamics under uncontrolled conditions. Furthermore, considering factors such as the initial status of the pursuit spacecraft, initial relative distance, transfer time, and maneuverability, the impulsive OPEG problem with limited action sets is numerically solved using game tree search. The findings of this study showcase the efficacy of game tree search in addressing impulsive OPEG problems with limited action sets. The study also demonstrates that the initial pursuing orientation selection at the start of the game plays a crucial role in increasing the success rate of pursuit. The research findings of this study have important implications for future practical engineering applications.

Approximate Optimal Strategy for Multiagent System Pursuit–Evasion Game

Approximate Optimal Strategy for Multiagent System Pursuit–Evasion Game

OPTIMAL NUMBER OF PURSUERS IN THE GAME ON THE 1-SKELETON OF TESSERACT

In the paper, we study pursuit and evasion differential games within a four-dimensional cube, where all the players move along the edges. The problem is to find the optimal number of pursuers in the game, to construct strategies for the pursuers in pursuit game, and evasion strategy in evasion game.

Modeling the Solution of the Pursuit–Evasion Problem Based on the Intelligent–Geometric Control Theory

An important action-planning problem is considered for participants of the pursuit–evasion game with multiple pursuers and a high-speed evader. The objects of study are mobile robotic systems and specifically small unmanned aerial vehicles (UAVs). The problem is complicated by the presence of significant wind loads that affect the trajectory and motion strategies of the players. It is assumed that UAVs have limited computing resources, which involves the use of computationally fast and real-time heuristic approaches. A novel and rapidly developing intelligent–geometric theory is applied to address the discussed problem. To accurately calculate the points of the participant’s rapprochement, we use a geometric approach based on the construction of circles or spheres of Apollonius. Intelligent control methods are applied to synthesize complex motion strategies of participants. A method for quickly predicting the evader’s trajectory is proposed based on a two-layer neural network containing a new activation function of the “s-parabola” type. We consider a special backpropagation training scheme for the model under study. A simulation scheme has been developed and tested, which includes mathematical models of dynamic objects and wind loads. The conducted simulations on pursuit–evasion games in close to real conditions showed the prospects and expediency of the presented approach.