Preference-based multi-objective multi-agent path finding

Ho, Florence; Nakadai, Shinji

doi:10.1007/s10458-022-09593-3

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…For instance, one strategy is to recursively solve conflicts between pairs of agents. 8 Other approaches have proposed to incorporate the preferences of a decision maker over the multi-agent planning problem, 9 and have developed adaptive methods that can bound the sub-optimal cost of the solution. 10 The problem is also referred to as multi agent path finding (MAPF) and hierarchical approaches have been developed where a spatial hierarchy to partition the environment into smaller sub-instances is used, and bounded-suboptimal MAPF solvers are then applied to each sub-instance.…”

Section: Multi-agent Path Planningmentioning

confidence: 99%

Spatially distributed lane planning for navigation in 3D environments

Sharma

Kallmann

2023

Computer Animation & Virtual

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This article introduces the new problem of planning spatially distributed lanes for supporting multi‐agent navigation applications in 3D environments. Our proposed approach computes the max‐flow of a 3D medial axis representation of the environment in order to globally compute collision‐free lanes exploring the entire free space of the volumetric scene. Our method addresses agent clearance and path dispersion in order to provide a comprehensive solution to globally compute lanes to be used by multiple agents in 3D environments. By selecting the desired lane dispersion our approach offers an intuitive and powerful way to explore variations in the computed collections of lanes. Dispersion is addressed with a combination of new techniques based on max flow computation, clearance‐based path separation, and adaptive shortcut‐based smoothing.

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Section: Multi-agent Path Planningmentioning

confidence: 99%

Spatially distributed lane planning for navigation in 3D environments

Sharma

Kallmann

2023

Computer Animation & Virtual

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“…Ho and Nakadai [32] have presented a set of criteria that can be utilized to evaluate specialized modeling languages designed for Multi-Agent Systems (MASs), encompassing both existing and prospective languages. The assessment primarily centers on language and its associated tools, and consequently presents both qualitative and quantitative findings.…”

Section: Agent Programming Languages Comparisonmentioning

confidence: 99%

An Analysis of Multi Agent Systems Agent Based Programming

Кhusein

2023

JCNS

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The effectiveness of agent-based modeling as a simulation modeling methodology has resulted in its application in diverse settings, including the resolution of pragmatic business challenges, in recent times. The domain of symbolic artificial intelligence, which investigates intelligent and self-governing entities, is preoccupied with the mechanisms by which these entities arrive at determinations regarding their conduct in reaction to, or in expectation of, stimuli from the external environment. The scope of the methods employed encompasses a diverse array of techniques, spanning from negotiations to agent simulations, as well as multi-agent argumentation and planning. The present article scrutinizes the utilization of agent-based computing in multi-agent systems and provides an all-encompassing analysis of the relevant literature. This study delves into the examination of both traditional and contemporary agent programming languages, including their respective extensions, comparative analyses, and instances of their application in published literature.

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“…The agent's primary function is to facilitate communication between the power system's software and hardware. The term "multi-agent system" [1] is used to describe a network in which several agents work together to achieve a common goal. The Multi-Agent's (MA's) fundamental characteristics remain constant across contexts.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Multi Agent System and Its Applications in Power System Engineering

Dong

2023

JCIMS

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An Intelligent Agent (IA) is a type of autonomous entity in the field of Artificial Intelligence (AI) that gathers information about its surroundings using sensors, takes action in response to that information using actuators ("agent" part), and guides its behavior to achieve predetermined results (i.e. it is rational). Agents that are both intelligent and able to learn or utilize information to accomplish their tasks would be ideal. Similar to how economists study agents, cognitive scientists, ethicists, philosophers of practical reason and researchers in a wide range of other disciplines study variations of the IAmodel used in multidisciplinary socio-cognitive modelling and computer social simulation models. In this article, the term "Multi-Agent System" (MAS) has been used to refer to a system in which two or more autonomous entities communicate with one another. The key objective of this research is to provide a critical analysis of MAS and its applications in power systems. A case study to define the application of MAS in power system is also provided, using a critical implementation of fuzzy logic controllers.

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Preference-based multi-objective multi-agent path finding

Cited by 5 publications

References 21 publications

Spatially distributed lane planning for navigation in 3D environments

Spatially distributed lane planning for navigation in 3D environments

An Analysis of Multi Agent Systems Agent Based Programming

A Survey on Multi Agent System and Its Applications in Power System Engineering

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