A rule-based argumentation framework for distributed contextual reasoning in dynamic environments

In this study, we tackled the problem of distributed reasoning in environments in which agents may have incomplete and inconsistent knowledge. Conflicts between agents are resolved through defeasible argumentation-based semantics with a preference function. Support for dynamic environments, where agents constantly enter and leave the system, was achieved by means of rules whose premises can be held by arbitrary agents. Moreover, we presented a formalism that enables agents to share information about their current situation or focus when issuing queries to other agents. This is necessary in environments where agents have a partial view of the world and must be able to cooperate with one another to reach conclusions. Hence, we presented the formalization of a multi-agent system and the argument construction and semantics that define its reasoning approach. Using example scenarios, we demonstrated that our system enables the modeling of a broader range of scenarios than related work.

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Active Surveillance of Dynamic Environments using a Multi-Agent System

IFAC Proceedings Volumes ◽

10.3182/20100906-3-it-2019.00005 ◽

2010 ◽

Vol 43 (16) ◽

pp. 13-18 ◽

Cited By ~ 5

Author(s):

Annalisa Milella ◽

Donato Di Paola ◽

Pier Luigi Mazzeo ◽

Paolo Spagnolo ◽

Marco Leo ◽

...

Keyword(s):

Active Surveillance ◽

Dynamic Environments ◽

Multi Agent System ◽

Agent System ◽

Multi Agent

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Towards a hybrid software architecture and multi-agent approach for autonomous robot software

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417716088 ◽

2017 ◽

Vol 14 (4) ◽

pp. 172988141771608 ◽

Cited By ~ 7

Author(s):

Shuo Yang ◽

Xinjun Mao ◽

Sen Yang ◽

Zhe Liu

Keyword(s):

Software Architecture ◽

Autonomous Robots ◽

Dynamic Environment ◽

Dynamic Environments ◽

Autonomous Robot ◽

Multi Agent System ◽

Development Framework ◽

Multi Agent ◽

Agent Development ◽

Sense Model

To support robust plan execution of autonomous robots in dynamic environments, autonomous robot software should include adaptive and reactive capabilities to cope with the dynamics and uncertainties of the evolving states of real-world environments. However, conventional software architectures such as sense-model-plan-act and behaviour-based paradigms are inadequate for meeting the requirements. A lack of sensing during acting in the sense-model-plan-act paradigm makes the software slow to react to run-time contingencies, whereas the behaviour-based architectures typically fall short in planning of long-range steps and making optimized plan adaptations. This article proposes a hybrid software architecture that maintains both adaptivity and reactivity of robot behaviours in dynamic environments. To implement this architecture, we further present the multi-agent development framework known as AutoRobot, which views the robot software as a multi-agent system in which diverse agent roles collaborate to achieve software functionalities. To demonstrate the applicability and validity of our concrete framework and software architecture, we conduct an experiment to implement a typical case, for example, a robot that autonomously picks up and drops off dishes for remote guests, which requires the robot to plan and navigate in a highly dynamic environment and can adapt its behaviours to unexpected situations.

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Genotype coding, diversity, and dynamic environments: A study on an evolutionary neural network multi-agent system

2014 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec.2014.6900593 ◽

2014 ◽

Cited By ~ 1

Author(s):

Jaime J. Davila

Keyword(s):

Neural Network ◽

Dynamic Environments ◽

Multi Agent System ◽

Agent System ◽

Evolutionary Neural Network ◽

Multi Agent

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Energy-Efficient Navigation of an Autonomous Swarm with Adaptive Consciousness

Remote Sensing ◽

10.3390/rs13061059 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1059

Author(s):

Jawad Naveed Yasin ◽

Huma Mahboob ◽

Mohammad-Hashem Haghbayan ◽

Muhammad Mehboob Yasin ◽

Juha Plosila

Keyword(s):

Energy Efficient ◽

Self Awareness ◽

Multi Agent System ◽

Energy Aware ◽

Safe Navigation ◽

Turn On ◽

Moving Obstacles ◽

Management Algorithm ◽

Multi Agent ◽

Share Information

The focus of this work is to analyze the behavior of an autonomous swarm, in which only the leader or a dedicated set of agents can take intelligent decisions with other agents just reacting to the information that is received by those dedicated agents, when the swarm comes across stationary or dynamic obstacles. An energy-aware information management algorithm is proposed to avoid over-sensation in order to optimize the sensing energy based on the amount of information obtained from the environment. The information that is needed from each agent is determined by the swarm’s self-awareness in the space domain, i.e., its self-localization characteristics. A swarm of drones as a multi-agent system is considered to be a distributed wireless sensor network that is able to share information inside the swarm and make decisions accordingly. The proposed algorithm reduces the power that is consumed by individual agents due to the use of ranging sensors for observing the environment for safe navigation. This is because only the leader or a dedicated set of agents will turn on their sensors and observe the environment, whereas other agents in the swarm will only be listening to their leader’s translated coordinates and the whereabouts of any detected obstacles w.r.t. the leader. Instead of systematically turning on the sensors to avoid potential collisions with moving obstacles, the follower agents themselves decide on when to turn on their sensors, resulting in further reduction of overall power consumption of the whole swarm. The simulation results show that the swarm maintains the desired formation and efficiently avoids collisions with encountered obstacles, based on the cross-referencing feedback between the swarm agents.

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