JAPL: the JADE Agent Programming Language

Bahaj, Mohamed; Soklabi, Abdellatif

doi:10.4304/jetwi.5.3.272-277

Cited by 2 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It uses Jena 15 for ontology model manipulation and allows to model the agent knowledge using OWL and to exchange messages with OWL and SPARQL as content language. Ontologies are modelled with Protégé 16 following the CommonKADS knowledge and engineering methodology [310]; the MAS design is based on AUML [30] and MAScommonKADS [196].…”

Section: Agentowl (2006)mentioning

confidence: 99%

See 1 more Smart Citation

Logic-based technologies for multi-agent systems: a systematic literature review

Calegari

Ciatto

Mascardi

et al. 2020

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computer-scientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI”—in particular, logic-based ones—will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones.

show abstract

Section: Agentowl (2006)mentioning

confidence: 99%

“…https://jena.apache.org/documentation/ontologyd, last accessed in April 2020 16. https://protege.stanford.edu, last accessed in April 2020.…”

mentioning

confidence: 99%

Logic-based technologies for multi-agent systems: a systematic literature review

Calegari

Ciatto

Mascardi

et al. 2020

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

show abstract

“…AIL is a toolkit of Java classes designed to support the implementation of BDI (Belief, Desire, and Intention) programming languages that allows for model checking of programs implemented in these BDI languages. Numerous BDI languages have been ported to the AIL platform, including: Jason [11] implementation of AgentSpeak [12], JAPL [13], SAAPL [14] and GOAL [15]. The AIL contains data structures to store its internal state comprising: a belief base, a plan library, a set of intentions, and other temporary state information which can be adapted to the operational semantics of specifc BDI languages.…”

Section: Agent Infrastructure Layer (Ail)mentioning

confidence: 99%

Agile robotic planning with Gwendolen

Michaloski¹

2022

View full text Add to dashboard Cite

The future of robotics foresees autonomous behavior that can complete tasks intelligently, with a focus on adaptability, flexibility, and versatility. In such systems, it is critical for robots to quickly and safely perform an operation. However, such aptitude is not limited to the speed of solving tasks, but also requires other qualities such as adeptly detecting and recovering from task irregularities, overcoming unforeseen task barriers by replanning to achieve stated goals, and adroitly adapting to dynamic environments such as changing light illumination, noisy sensors, or unexpected conditions. These intelligent characteristics define robot agility (not to be confused with robot agility akin to dexterity), and refer to approaches that allow robotic systems to be flexible and capable of re-tasking in the face of a changing and often unpredictable environment. Because robot task agility requires sophisticated dynamic and continuous planning and replanning, the Gwendolen intelligent agent programming language is studied as a high-level robot planner. In this report, we develop a manufacturing kitting case study to research the operation of Gwendolen planning. The case study uses the combination of Gwendolen, Canonical Robot Command Language (CRCL), Robot Operating System (ROS), and Gazebo software components to simulate and evaluate robot planning. Several Agile Robotics for Industrial Applications Competition (ARIAC) kitting agility challenges are used to evaluate Gwendolen planning under various levels of operational duress. Both the benefits and shortcomings will be reviewed.

show abstract

JAPL: the JADE Agent Programming Language

Cited by 2 publications

References 12 publications

Logic-based technologies for multi-agent systems: a systematic literature review

Logic-based technologies for multi-agent systems: a systematic literature review

Agile robotic planning with Gwendolen

Contact Info

Product

Resources

About