A Planning Based Agent Programming Language Supporting Environment Modeling

Hashmi, Muhammad Adnan; Seghrouchni, Amal El Fallah; Akram, M. Usman

doi:10.1109/wi-iat.2015.22

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…In [35] an AOP language called Planning based Language for Agents and Computational Environments (PLACE) adds AI planning capability to agents. PLACE has a syntactic structure close to BDI, while the planning is done in a Hierarchical Task Network (HTN) planner.…”

Section: General-purpose Aplsmentioning

confidence: 99%

A Review of Agent-Based Programming for Multi-Agent Systems

Cardoso

Ferrando

2021

Computers

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Intelligent and autonomous agents is a subarea of symbolic artificial intelligence where these agents decide, either reactively or proactively, upon a course of action by reasoning about the information that is available about the world (including the environment, the agent itself, and other agents). It encompasses a multitude of techniques, such as negotiation protocols, agent simulation, multi-agent argumentation, multi-agent planning, and many others. In this paper, we focus on agent programming and we provide a systematic review of the literature in agent-based programming for multi-agent systems. In particular, we discuss both veteran (still maintained) and novel agent programming languages, their extensions, work on comparing some of these languages, and applications found in the literature that make use of agent programming.

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Section: General-purpose Aplsmentioning

confidence: 99%

A Review of Agent-Based Programming for Multi-Agent Systems

Cardoso

Ferrando

2021

Computers

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“…The environment modelling approach that we have adopted bears a few resemblances with the model of artifacts proposed in [28] and [29]. Major dissimilarity between these two approaches is that in [29] an artifact denotes a physical or computational entity in the environment (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Major dissimilarity between these two approaches is that in [29] an artifact denotes a physical or computational entity in the environment (e.g. a mouse, a sensor, a web-service etc) whereas in [28] an artifact connotes a conceptual or logical entity (e.g. a car, a house, a place etc).…”

Section: Introductionmentioning

confidence: 99%

Runtime Reasoning of Requirements for Self-Adaptive Systems Using AI Planning Techniques

Hassan¹,

Qureshi²,

Hashmi³

et al. 2018

ijacsa

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Over the years, the domain of Self-Adaptive Systems (SAS) has gained significant importance in software engineering community. Such SAS must ensure high customizability and at the same time effective reasoning to meet their objectives by meeting end-user goals more effectively and efficiently. In this context, techniques related to Automated Planning have acquired substantial precedence owing to their adaptability to diverse scenarios based upon their enhanced knowledge extraction from available Knowledge Base. These AI planning techniques help in supporting self-adaptation mechanism of SAS. We have investigated these techniques to perform runtime reasoning of SAS requirements. This paper proposes an architecture for implementing the reasoning component of previously proposed Continuous Adaptive Requirement Engineering (CARE) framework. The proposed architecture has been experimentally verified by implementation of a prototype application using JSHOP2 (Java implementation of SHOP2, an HTN Planner).

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