Reflecting on Agent Programming with AgentSpeak(L)

Collier, Rem W.; Russell, Seán; Lillis, David

doi:10.1007/978-3-319-25524-8_22

Cited by 25 publications

(10 citation statements)

References 14 publications

(1 reference statement)

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“…Primary references: [88] Other references describing / exploiting the technology: [139,299] URL: http://astralanguage.com http://github.com/remcollier/astra MAS perspective Similarly to Jason, ASTRA allows developers to define agent plans in a declarative way; yet, unlike Jason, it offers a tighter integration with the Java language and runtime. Java objects and types can in fact directly be used within ASTRA scripts.…”

Section: Astra (2015)mentioning

confidence: 99%

“…Primary references: [299] Other references describing / exploiting the technology: [45,88] URL: http://github.com/agentspeakers/jason-er MAS perspective AgentSpeak(ER) is an extension of AgentSpeak(L) featuring plan encapsulation, i.e. the possibility to define plans that fully encapsulate the strategy to achieve the corresponding goals, integrating both the proactive and the reactive behaviour.…”

Section: Agentspeak(er) and Jason-er (2019)mentioning

confidence: 99%

“…AgentSpeak(ER) agents expose "g-plans" that can be repeatedly re-executed until a given goal-condition about the beliefs of some agent is met. AgentSpeak(ER) comes with two prototype implementations, one based on ASTRA by Collier et al [88] and the other one based on Jason. Jason-ER 11 provides the syntactical and semantic extensions needed by Jason to support AgentSpeak(ER).…”

Section: Agentspeak(er) and Jason-er (2019)mentioning

confidence: 99%

See 2 more Smart Citations

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

Calegari

Ciatto

Mascardi

et al. 2020

Auton Agent Multi-Agent Syst

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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.

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Section: Astra (2015)mentioning

confidence: 99%

Section: Agentspeak(er) and Jason-er (2019)mentioning

confidence: 99%

Section: Agentspeak(er) and Jason-er (2019)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

show abstract

“…AgentSpeak(L) was essentially an abstract programming language with a precise operational semantics [14,117], though an implementation in Prolog was later developed by Winikoff [151]. Although it was an abstract language (initially) lacking an implementation, AgentSpeak(L) was influential in the design of many other languages and platforms, notably Jason [18] and ASTRA [37], as well as further work on abstract BDI programming, in particular CAN [155].…”

Section: Historical Development Of Bdi-based Agent Programming Languagesmentioning

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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“…We strongly believe that our approach is quite general and that it can be applied to obtain multi‐agent models and simulations for a large variety of dynamic systems. Moreover, although we demonstrate this approach only for the Jason AOP, we have reasons to believe that it can be used with other available BDI‐based AOP frameworks, like, for example, ASTRA (see Collier, Russell, & Lillis, ). According to the recent reviews (C. Bădică, Budimac, Burkhard, & Ivanović, ; Kravari & Bassiliades, ; Leon, Paprzycki, & Ganzha, ), a multitude of agent programming languages and platforms has been proposed by the agent research community.…”

Section: Introductionmentioning

confidence: 99%

Multi‐agent modelling and simulation of graph‐based predator–prey dynamic systems: A BDI approach

et al. 2018

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We propose a new framework based on Belief‐Desire‐Intention multi‐agent systems for the macroscopic modelling and simulation of continuous dynamic systems. The main idea is to break down the target system model into a collection of autonomous and loosely coupled interacting components endowed with clean message‐based interfaces and local intelligence. Each component is then mapped to a Belief‐Desire‐Intention agent that captures its state as a set of logical facts and its behavioural patterns as a set of plans. The system model can be described as a multi‐agent programme that is specified using the state‐of‐the‐art Jason agent‐oriented programming language. The approach is evaluated by considering a generalized graph‐based model of predator–prey systems. Our approach supports the configuration of the multi‐agent model with various differential equations integration methods, as required by the specific problem.

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Reflecting on Agent Programming with AgentSpeak(L)

Cited by 25 publications

References 14 publications

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

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

Agent programming in the cognitive era

Multi‐agent modelling and simulation of graph‐based predator–prey dynamic systems: A BDI approach

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