AgentSpeak(XL)

Bordini, Rafael H.; Bazzan, Ana L. C.; Jannone, Rafael de O.; Basso, Daniel M.; Vicari, Rosa María; Lesser, Victor

doi:10.1145/545056.545122

Cited by 47 publications

(4 citation statements)

References 12 publications

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“…We consider a robotic packaging scenario, extended from [24], where a robot packs products and moves them to a storage area. Products have specific temperatures and must be packed in a suitable wrapping bag to prevent decay.…”

Section: Smart Manufacturing Examplementioning

confidence: 99%

“…To manage intention interleaving, researchers tend to employ external tools to help the agent to pursue multiple intentions in parallel. For example, the work of [24] compiles agent programs to TAEMS (Task Analysis, Environment Modelling, and Simulation) framework to represent the coordination aspects of problems such as "enables" and "hinders" relations between tasks. A Design-To-Criteria scheduler is then used for intention selection to determine the full set of decisions that the agent needs to perform.…”

Section: Related Workmentioning

confidence: 99%

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Quantitative modelling and analysis of BDI agents

Archibald,

Calder,

Sevegnani

et al. 2023

Softw Syst Model

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Belief–desire–intention (BDI) agents are a popular agent architecture. We extend conceptual agent notation (Can)—a BDI programming language with advanced features such as failure recovery and declarative goals—to include probabilistic action outcomes, e.g. to reflect failed actuators, and probabilistic policies, e.g. for probabilistic plan and intention selection. The extension is encoded in Milner’s bigraphs. Through application of our BigraphER tool and the PRISM model checker, the probability of success (intention completion) under different probabilistic outcomes and plan/event/intention selection strategies can be investigated and compared. We present a smart manufacturing use case. A significant result is that plan selection has limited effect compared with intention selection. We also see that the impact of action failures can be marginal—even when failure probabilities are large—due to the agent making smarter choices.

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Section: Smart Manufacturing Examplementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Quantitative modelling and analysis of BDI agents

Archibald,

Calder,

Sevegnani

et al. 2023

Softw Syst Model

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“…Finally, Bordini et al [2002] implemented one of AgentSpeak's selection functions by integrating quantitative reasoning using TAEMS [Decker, 1996]. A key difference between our work and theirs is that they focused on intention selection, i.e.…”

Section: Literaturementioning

confidence: 99%

No Pizza for You: Value-based Plan Selection in BDI Agents

Cranefield

Winikoff

Dignum

et al. 2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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Autonomous agents are increasingly required to be able to make moral decisions. In these situations, the agent should be able to reason about the ethical bases of the decision and explain its decision in terms of the moral values involved. This is of special importance when the agent is interacting with a user and should understand the value priorities of the user in order to provide adequate support. This paper presents a model of agent behavior that takes into account user preferences and moral values.

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“…Fifth, the elegance of the AgentSpeak notation is particularly appealing to the symbolic representation of beats and behaviors. Last, Jason is also a reactive planning language [24]. Reference [15, p.105] comments about the importance of the use of a reactive planning language in the interactive drama Façade:…”

Section: Dramasmentioning

confidence: 99%

An Open Source Architecture for Building Interactive Dramas

Muller¹

2011

2011 Brazilian Symposium on Games and Digital Entertainment

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This work presents DGiovanni, an open source multi-agent architecture for building interactive dramas. The architecture has been developed in JAVA and uses the Jason's BDI engine, being the Jason's agent-oriented programming language utilized as the means for performing the drama management and for authoring the characters' behaviors. Additionally, it makes use of ontologies to support the creation of different stories and to feed the system with story-related information. Also, the architecture can help in the development and research of interactive dramas, by including several facilities for developing the story. Finally, in order to demonstrate the use of the architecture, it has been also created a story implementation that supports some interactivity mechanisms such as a simple Natural Language Processing.

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AgentSpeak(XL)

Cited by 47 publications

References 12 publications

Quantitative modelling and analysis of BDI agents

Quantitative modelling and analysis of BDI agents

No Pizza for You: Value-based Plan Selection in BDI Agents

An Open Source Architecture for Building Interactive Dramas

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