A Game Theoretic Approach to Swarm Robotics

Givigi, Sidney N.; Schwartz, Howard M.

doi:10.1155/2006/183949

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context it becomes difficult to abstract properties and reuse results across different works. However, by some mathematical abstraction, it is still possible to identify and perform an analysis of typical classes of swarm behaviors that include, among others, foraging [79,80,81,82], coverage [83,84,85,86], aggregation and pattern formation [87,88,89,80], and cooperative tracking [83,90,91] (an extensive, and well discussed list of swarm behaviors can be found in [92,93], and an extension in [13]).…”

Section: Challenges Deriving Formal Propertiesmentioning

confidence: 99%

Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends

Schranz

Schmickl

et al. 2021

Swarm and Evolutionary Computation

117

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Section: Challenges Deriving Formal Propertiesmentioning

confidence: 99%

Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends

Schranz

Schmickl

et al. 2021

Swarm and Evolutionary Computation

117

View full text Add to dashboard Cite

show abstract

“…Givigi et al uses game theory for the modeling of the agents' behaviors [10], [11]. Inspired by the conventional game theory, evolutionary game theory has been developed to clarify a biological evolution of agents' behaviors [12], which is expected to be applied to some research areas such as economics, cognitive science and so on.…”

Section: Introductionmentioning

confidence: 99%

Order formations in multi-agent search problem: A game theoretic approach

Saitō

Hatanaka

Fujita

2010

Proceedings of the 2010 American Control Conference

View full text Add to dashboard Cite

In this paper we investigate a search problem for a swarm of agents to find a target which appears randomly and stays for a fixed time interval. We assume that there are two search areas and the target appears in either of them. Under the situation, the objective of this paper is to achieve two types of orders: macro and micro orders. The former means that the population share of agents converges to an ordered value, and the latter means that the agents' motion converges to an ordered one. In order to achieve macro order, we first present a probabilistic decision-making model on which area to search called Win-Stay-Lose-Shift. Then, we prove convergence of the expectation value of population share based on the knowledge of evolutionary game theory when two specific payoff structures are taken. In order to achieve micro order, we next present a search strategy used after the area to be searched is decided, and prove that agents' trajectories converge to periodic ones respectively. Finally, simulation results show the validity of the proposed method.

show abstract