ARGO: An Extended Jason Architecture that Facilitates Embedded Robotic Agents Programming

Pantoja, Carlos Eduardo; Stabile, Márcio Fernando; Lazarin, Nilson Mori; Sichman, Jaime Simão

doi:10.1007/978-3-319-50983-9_8

Cited by 25 publications

(22 citation statements)

References 12 publications

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“…Each approach implements the business rules described previously and has its EMPC measured based on 200 messages sent to RML. The Table 2 shows the results obtained based on the following configuration: Agent Approach: It employs one Physical Agent using ARGO, a JaCaMo extension [ 37 ] for interfacing hardware and collecting information from the home garden; a mediator agent, which receives the information collected from the Physical Agent and processes them; and one Communicator Agent, which receives the information from the mediator and sends to the RML. The EMPC is composed of the Physical, Mediator, and Communicator Agents’ processing cost.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Engineering Approaches for Programming Agent-Based IoT Objects Using the Resource Management Architecture

Brandão

Lima

Pantoja

et al. 2021

Sensors

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The Internet of Things (IoT) allows the sharing of information among devices in a network. Hardware evolutions have enabled the employment of cognitive agents on top of such devices, which could help to adopt pro-active and autonomous IoT systems. Agents are autonomous entities from Artificial Intelligence capable of sensing (perceiving) the environment where they are situated. Then, with these captured perceptions, they can reason and act pro-actively. However, some agent approaches are created for a specific domain or application when dealing with embedded systems and hardware interfacing. In addition, the agent architecture can compromise the system’s performance because of the number of perceptions that agents can access. This paper presents three engineering approaches for creating IoT Objects using Embedded Multi-agent systems (MAS)—as cognitive systems at the edge of an IoT network—connecting, acting, and sharing information with a re-engineered IoT architecture based on the Sensor as a Service model. These engineering approaches use Belief-Desire-Intention (BDI) agents and the JaCaMo framework. In addition, it is expected to diversify the designers’ choice in applying embedded MAS in IoT systems. We also present a case study to validate the whole re-engineered architecture and the approaches. Moreover, some performance tests and comparisons are also presented. The study case shows that each approach is more or less suitable depending on the domain tackled. The performance tests show that the re-engineered IoT architecture is scalable and that there are some trade-offs in adopting one or another approach. The contributions of this paper are an architecture for sharing resources in an IoT network, the use of embedded MAS on top IoT Objects, and three engineering approaches considering agent and artifacts dimensions.

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Section: Experimental Evaluationmentioning

confidence: 99%

Engineering Approaches for Programming Agent-Based IoT Objects Using the Resource Management Architecture

Brandão

Lima

Pantoja

et al. 2021

Sensors

Self Cite

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“…Ziafati et al [163] have proposed several extensions to BDI-based agent programming languages to allow the connection of agent programs to robots, including subscribing to events of interest at run-time, asynchronous reception of events, maintaining histories of events and run-time querying of the histories. Pantoja et al [114] adopt a similar approach in the ARGO architecture for programming embedded robotic agents using Jason. Their approach builds on work by Stabile and Sichman [135], and uses perception filters to ensure the real-time processing necessary for robot control, e.g., to prevent collisions effectively.…”

Section: Ai In the Sense Phasementioning

confidence: 99%

Agent programming in the cognitive era

Bordini

Seghrouchni

Hindriks

et al. 2020

Auton Agent Multi-Agent Syst

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It is claimed that, in the nascent 'Cognitive Era', intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., 'AI as a service', exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., 'AI embedded into agents' raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs.

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“…There is an extension of Jason's agents capable of controlling devices such as sensors and actuators connected to microcontrollers named ARGO [13]. This customized architecture is able of capturing perceptions and send them to agents without any interference of the programmer and agents are able of executing actions using actuators without worrying about what kind of hardware is being employed.…”

Section: B Framework Jasonmentioning

confidence: 99%

An Architecture for the Development of Ambient Intelligence Systems Managed by Embedded Agents

Pantoja¹,

Soares²,

Viterbo³

et al. 2018

International Conferences on Software Engineering and Knowledge Engineering

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Ubiquitous systems consider the use of electronic components for enhancing daily objects with some kind of computational intelligence for aiding users in their tasks pervasively. Ambient Intelligence (AmI) is a branch of ubiquitous computing that provides an environment full of interconnected devices and it can provide data communication, inference mechanism based on context information and collaboration among system's devices. Similarly, the Internet of Things (IoT) provides uniquely identified devices or things in a network for helping users in their activities. Multi-Agent Systems (MAS) are intelligent systems where agents are responsible for reasoning, competing and using resources to achieve desirable goals pro-actively and autonomously. Agents have been employed in some approaches and works during the last years, but none of them considered embedded MAS responsible for smart devices in an AmI system running over an IoT network. Besides, it is also interesting that agents of the embedded MAS can interact, sharing information with agents situated in another embedded MAS using the IoT network to learn from user's experiences. This paper proposes an architecture for the development of AmI systems using embedded MAS for interfacing with sensors and actuators in a heterogenous network using an IoT middleware.

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ARGO: An Extended Jason Architecture that Facilitates Embedded Robotic Agents Programming

Cited by 25 publications

References 12 publications

Engineering Approaches for Programming Agent-Based IoT Objects Using the Resource Management Architecture

Engineering Approaches for Programming Agent-Based IoT Objects Using the Resource Management Architecture

Agent programming in the cognitive era

An Architecture for the Development of Ambient Intelligence Systems Managed by Embedded Agents

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