LightJason

Aschermann, Malte; Kraus, Philipp; Müller, Jörg P.

doi:10.1007/978-3-319-59294-7_6

Cited by 15 publications

(4 citation statements)

References 10 publications

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“…Modularization can significantly facilitate and improve such vision. Its adoption in the context of BDI-AOP has been widely discussed and developed in the literature ( Zanbar & Kaminka, 2019 ; da Rocha Costa, 2018 ; Ricci et al, 2019 ; Aschermann, Kraus & Müller, 2017 ; Busetta et al, 1999 ; Braubach, Pokahr & Lamersdorf, 2006 ; Dastani & Steunebrink, 2009 ; Cap, Dastani & Harbers, 2011 ; Dastani, Mol & Steunebrink, 2009 ; Madden & Logan, 2010 ; Ortiz-Hernandez, Guerra-Hernandez & Hoyos-Rivera, 2013 ; Hindriks, 2008 ; Van Riemsdijk et al, 2006 ; Ricci, Piunti & Viroli, 2009 ). As stated by Suryanarayana, Samarthyam & Sharma (2015) , modularization can be understood as “ the logical partitioning of a software design so that the design becomes easy to understand and maintain ”.…”

Section: Introductionmentioning

confidence: 99%

Modularization in Belief-Desire-Intention agent programming and artifact-based environments

Ortiz-Hernández

Guerra‐Hernández

Hübner

et al. 2022

PeerJ Computer Science

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This article proposes an extension for the Agents and Artifacts meta-model to enable modularization. We adopt the Belief-Desire-Intention (BDI) model of agency to represent independent and reusable units of code by means of modules. The key idea behind our proposal is to take advantage of the syntactic notion of namespace, i.e., a unique symbol identifier to organize a set of programming elements. On this basis, agents can decide in BDI terms which beliefs, goals, events, percepts and actions will be independently handled by a particular module. The practical feasibility of this approach is demonstrated by developing an auction scenario, where source code enhances scores of coupling, cohesion and complexity metrics, when compared against a non-modular version of the scenario. Our solution allows to address the name-collision issue, provides a use interface for modules that follows the information hiding principle, and promotes software engineering principles related to modularization such as reusability, extensibility and maintainability. Differently from others, our solution allows to encapsulate environment components into modules as it remains independent from a particular BDI agent-oriented programming language.

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Section: Introductionmentioning

confidence: 99%

Modularization in Belief-Desire-Intention agent programming and artifact-based environments

Ortiz-Hernández

Guerra‐Hernández

Hübner

et al. 2022

PeerJ Computer Science

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“…Mobile systems can be designed and developed on an elevated abstraction level by making use of high-level agent concepts like beliefs, plans, and goals. Unfortunately, cognitive agent frameworks [7,9,10] lack satisfactory support for the processing of sensor data. Today's mobile devices are equipped with a rich set of sensors that can be exploited to achieve system behavior that adapts itself to the current situation of the mobile user.…”

Section: Introductionmentioning

confidence: 99%

Stream-based perception for cognitive agents in mobile ecosystems

Dötterl¹,

Bruns²,

Dunkel³

et al. 2019

AIC

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Cognitive agent abstractions can help to engineer intelligent systems across mobile devices. On smartphones, the data obtained from onboard sensors can give valuable insights into the user's current situation. Unfortunately, today's cognitive agent frameworks cannot cope well with the challenging characteristics of sensor data. Sensor data is located on a low abstraction level and the individual data elements are not meaningful when observed in isolation. In contrast, cognitive agents operate on highlevel percepts and lack the means to effectively detect complex spatio-temporal patterns in sequences of multiple percepts. In this paper, we present a stream-based perception approach that enables the agents to perceive meaningful situations in low-level sensor data streams. We present a crowdshipping case study where autonomous, self-interested agents collaborate to deliver parcels to their destinations. We show how situations derived from smartphone sensor data can trigger and guide auctions, which the agents use to reach agreements. Experiments with real smartphone data demonstrate the benefits of stream-based agent perception.

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“…AgentSpeak formally describes the operational semantics of its reasoning cycle. There are several implementations of AgentSpeak, but Jason is the most prominently used [12]. Jason [1] is a Java-based interpreter for AgentSpeak that offers a comprehensive architecture for developing agents.…”

Section: Background: Agentspeak: a Logical Language For Bdi Agentsmentioning

confidence: 99%

“…A notable language is AgentSpeak, an abstract language introduced by Rao [11]. Jason [1] is a agent-oriented platform that realizes the abstract language of AgentSpeak, and has recently gained considerable attention in the field of agent development [12]. We use the terms AgentSpeak and Jason interchangeably throughout this thesis but will explicitly differentiate when necessary.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

Qualitative Uncertainty Reasoning in AgentSpeak

Vezina¹

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This dissertation proposes two novel approaches for extending AgentSpeak with qualitative uncertainty reasoning by integrating two dynamic variants of epistemic logic. These extensions address a crucial gap in the literature where qualitative approaches to uncertainty are seldom integrated into agent-oriented programming languages due to various challenges related to methodology, implementation, and computational complexity.The extensions provide various symbolic constructs that enable the modelling and reasoning of belief uncertainty. The significance of qualitative uncertainty reasoning is illustrated through a simple Minesweeper scenario and two complex uncertainty challenges from the 2019 Multi-Agent Programming Contest: uncertain navigation and agent identification. Given the ability to express qualitative uncertainty, we equip the agent with a more robust and effective way to plan and act under uncertainty.An in-depth evaluation of the performance and scalability of the proposed Agent-Speak extensions is provided, with a heavy focus on examining their impact on the agent's time-sensitive reasoning cycle. The results show that these extensions provide a tractable and computationally feasible approach to extending AgentSpeak with the ability to manage the statics and dynamics of uncertainty. i This dissertation is dedicated to my love, Sandra.A constant source of support and inspiration in every way imaginable, you have always given me the strength and determination to persevere and overcome any challenges that have arisen. I love you and will forever be grateful for the time and experiences we have and will endure together.To Mrs. Morley, I cannot thank you enough for the support you have given to both Sandra and I. Your belief in our potential and your constant push for us to aim higher and strive for greater things will forever impact our lives. It was through the support, encouragement, and inspiration of you and Sandra that I gained the confidence and strength to pursue further education. I love you and will miss you dearly. You will forever be cherished, and your influence will continue to guide Sandra and I as we navigate the next phases of our lives. I would also like to express my gratitude to Professor Esfandiari, whose support, guidance, and encouragement have been invaluable. Your expertise and wisdom have played a crucial role in my academic and personal growth, and I am truly grateful for the opportunity to learn from and work alongside you.I am grateful to all who supported me during this journey, as you have helped to form the foundation of this dissertation.

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LightJason

Cited by 15 publications

References 10 publications

Modularization in Belief-Desire-Intention agent programming and artifact-based environments

Modularization in Belief-Desire-Intention agent programming and artifact-based environments

Stream-based perception for cognitive agents in mobile ecosystems

Qualitative Uncertainty Reasoning in AgentSpeak

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