Typhon - A Mobile Agents Framework for Real World Emulation in Prolog

Matani, Jatin; Nair, Shivashankar B.

doi:10.1007/978-3-642-25725-4_23

Cited by 16 publications

(4 citation statements)

References 8 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Naturally, every computing node would need a platform to host such agents, resources for the agents to execute and ensure their eventual dispatch to another node. Several such agent platforms exist [21]- [25]. Tartarus [26] is one such open source platform, which is written in SWI-Prolog.…”

Section: Tartarus -A Mobile Agent Platformmentioning

confidence: 99%

On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios

Nair¹,

Kulkarni²,

Joshi³

et al. 2022

Preprint

View full text Add to dashboard Cite

Federated Learning (FL) allows for collaboratively aggregating learned information across several computing devices and sharing the same amongst them, thereby tackling issues of privacy and the need of huge bandwidth. FL techniques generally use a central server or cloud for aggregating the models received from the devices. Such centralized FL techniques suffer from inherent problems such as failure of the central node and bottlenecks in channel bandwidth. When FL is used in conjunction with connected robots serving as devices, a failure of the central controlling entity can lead to a chaotic situation. This paper describes a mobile agent based paradigm to decentralize FL in multi-robot scenarios. Using Webots, a popular free open-source robot simulator, and Tartarus, a mobile agent platform, we present a methodology to decentralize federated learning in a set of connected robots. With Webots running on different connected computing systems, we show how mobile agents can perform the task of Decentralized Federated Reinforcement Learning (dFRL). Results obtained from experiments carried out using Q-learning and SARSA by aggregating their corresponding Q-tables, show the viability of using decentralized FL in the domain of robotics. Since the proposed work can be used in conjunction with other learning algorithms and also real robots, it can act as a vital tool for the study of decentralized FL using heterogeneous learning algorithms concurrently in multi-robot scenarios.

show abstract

Section: Tartarus -A Mobile Agent Platformmentioning

confidence: 99%

On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios

Nair¹,

Kulkarni²,

Joshi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We have used Typhon -A Mobile Agents Framework for Real World Emulation in Prolog [9] to achieve totally distributed and decentralized parallelism. The mobile agent takes the observation sequence (the recorded audio in vector quantized form) as payload to different nodes.…”

Section: Mobile Agentsmentioning

confidence: 99%

A mobile agents based distributed speech recognition engine for controlling multiple robots

Gupta

Verma

Bhattacharya

et al. 2015

Proceedings of the 2015 Conference on Advances in Robotics

View full text Add to dashboard Cite

Interaction with a robot has been an active area of research since the inception of robotics. Talking to a robot has always been considered the most natural way to communicate with it. But it is not always possible to have a full-fledged, standalone speech processing engine to be present on a robot or on a single machine. A dedicated system to convert the commands from audio to text is needed. However, as the number of commands and robots increases, it becomes necessary to eliminate all the single-point failure points in the system. Thus, distributed speech engine comes into picture. Also users may want to talk to the robot in different languages.The approach proposed in this paper is distributed, fault tolerant and scalable, such that any new recognition algorithm or language support can be added and used without any changes to the existing system.The work has been demonstrated on a freely available mobile agents based Internet of Things platform. However, any platform can be used.

show abstract

“…Mobile-C [Mostinckx et al 2009] is a purely C/C++ based agent framework which, due to its small code size, readily supports embedded devices. JINNI [Tarau 1999], Typhon [Matani and Nair 2011] and Tartarus [Semwal et al 2015;Semwal et al 2016], being Prolog based frameworks, facilitate rapid prototype development. The work reported herein uses Tartarus, a mobile agent framework for the development and management of static and mobile agents.…”

Section: Mobile Agent Frameworkmentioning

confidence: 99%

On Ordering Multi-Robot Task Executions within a Cyber Physical System

Semwal

Jha

Nair

2017

ACM Trans. Auton. Adapt. Syst.

Self Cite

View full text Add to dashboard Cite

With robots entering the world of Cyber Physical Systems (CPS), ordering the execution of allocated tasks during run-time becomes crucial. This is so because, in a real world, there can be several physical tasks that use shared resources that need to be executed concurrently. In this paper, we propose a mechanism to solve this issue of ordering task executions within a CPS which inherently handles mutual exclusion. The mechanism caters to a decentralized and distributed CPS comprising nodes such as computers, robots and sensor nodes, and uses mobile software agents that knit through them to aid the execution of the various tasks while also ensuring mutual exclusion of shared resources. The computations, communications and control, are achieved through these mobile agents. Physical execution of the tasks is performed by the robots in an asynchronous and pipelined manner without the use of a clock. The mechanism also features addition and deletion of tasks and insertion and removal of robots facilitating On-The-Fly Programming. As an application, a Warehouse Management System as a CPS has been implemented. The paper concludes with the results and discussions on using the mechanism in both emulated and real world environments.

show abstract

Typhon - A Mobile Agents Framework for Real World Emulation in Prolog

Cited by 16 publications

References 8 publications

On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios

On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios

A mobile agents based distributed speech recognition engine for controlling multiple robots

On Ordering Multi-Robot Task Executions within a Cyber Physical System

Contact Info

Product

Resources

About