A Multi-drones Target Tracing Strategy Based on the Pursuit-Evasion Game Formula

Yang, Kejie; Zhu, Ming; Guo, Xiao

doi:10.1109/cac57257.2022.10054992

Cited by 5 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And carried out SIMULINK modeling and simulation. We carried out SIMULINK modeling and simulation while combining the game-theoretic approach of the articles [23][24][25][26][27][28][29][30][31][32] and UAV modeling ideas.…”

Section: Discussionmentioning

confidence: 99%

Pursuit Problem of Unmanned Aerial Vehicles

Oleg,

Zhang

2023

Mathematics

View full text Add to dashboard Cite

The study examines scenarios involving a single pursuer tracking a single evader, as well as situations where multiple pursuers are involved in chasing multiple evaders. We formulate this problem as a search and pursuit problem for unmanned aerial vehicles (UAVs). Game theory offers a mathematical framework to model and examine strategic interactions involving multiple decision-makers. By employing game theory principles to address the search and pursuit problem, our objective is to optimize the efficiency of strategies for detecting and capturing unmanned aerial vehicles (UAVs).

show abstract

Section: Discussionmentioning

confidence: 99%

Pursuit Problem of Unmanned Aerial Vehicles

Oleg,

Zhang

2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…More recently, [80], [81] proposed a new model called the Game-Theoretic Utility Tree (GUT), combining the core principles of game theory and utility theory to achieve…”

Section: A Game-theoretic Utility Systemsmentioning

confidence: 99%

Research Status and Challenges on the Sustainable Development of Artificial Intelligence Courses from a Global Perspective

et al. 2023

View full text Add to dashboard Cite

The widespread application of artificial intelligence technology in various fields has made the sustainable development of artificial intelligence courses an important direction in the field of artificial intelligence education and teaching. Therefore, it is particularly important to conduct an in-depth analysis of the current research status of “artificial intelligence courses” from a global perspective. Firstly, this article clarifies the three stages of slow development, rapid development, and mature development of artificial intelligence curriculum research through the number and distribution years of the literature. It also conducts a co-authorship analysis on the distribution of countries, institutions, and authors of artificial intelligence curriculum research and identifies countries, institutions, and core authors that have made greater contributions to artificial intelligence curriculum research. Secondly, due to the involvement of artificial intelligence in many different fields of knowledge, an analysis is conducted on the journals that published papers on artificial intelligence courses. Finally, based on the analysis of keyword density and time span, the current research hotspots of artificial intelligence courses are summarized: artificial intelligence technology empowerment courses, two education directions at different stages of artificial intelligence courses, and teaching forms in the field of artificial intelligence courses. The current research trend of artificial intelligence courses is analyzed from three aspects: teaching format, teaching content, and teaching objects. This article provides a theoretical reference value and practical basis for future research and development in the field of artificial intelligence courses, while also providing experience for the efficient and sustainable development of artificial intelligence courses to a certain extent.

show abstract

“…It exhibits the fundamental properties of agents' perception, reasoning, planning, decision-making, learning, problem-solving, and communication in interaction with dynamic and complex environments [9,19]. Especially in the field of real-time strategy (RTS) game [3,23,24] and real-world implementation scenarios like robot-aided urban search and rescue (USAR) missions [12,21], agents need to dynamically change the strategies adapting to the current situations based on the environments and their expected utilities or needs [20,22].…”

Section: Introductionmentioning

confidence: 99%

A Strategy-Oriented Bayesian Soft Actor-Critic Model

Qin¹,

Parasuraman²

2023

Preprint

View full text Add to dashboard Cite

Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on the Bayesian chain rule to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method -soft actor-critic (SAC) and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare the proposed BSAC method with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the standard continuous control benchmarks -Hopper-v2, Walker2d-v2, and Humanoid-v2 -in MuJoCo with the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency.

show abstract

A Multi-drones Target Tracing Strategy Based on the Pursuit-Evasion Game Formula

Cited by 5 publications

References 18 publications

Pursuit Problem of Unmanned Aerial Vehicles

Pursuit Problem of Unmanned Aerial Vehicles

Research Status and Challenges on the Sustainable Development of Artificial Intelligence Courses from a Global Perspective

A Strategy-Oriented Bayesian Soft Actor-Critic Model

Contact Info

Product

Resources

About