Using event-driven lightweight DSC-based agents for MAS modelling

Fortino, Giancarlo; Garro, Alfredo; Mascillaro, Samuele; Russo, Wilma

doi:10.1504/ijaose.2010.032798

Cited by 36 publications

(16 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Researchers need to start experimenting on real robots in unknown and unstructured environments to prove the efficiency, the applicability and the reliability of the proposed approaches and to help in understanding the actual behavior of a swarm of robots in real world without any simplifications. Researchers can use agent programming methodologies and tools such the one proposed in Fortino et al (2005Fortino et al ( , 2010Fortino et al ( , 2012Fortino et al ( , 2015, with PROFETA language presented in to perform a rapid prototyping of their approach in order to investigate which design Band implementation choices are required before the real implementation. Also generic framework for distributed swarm computing can be used to model the swarm system as the one proposed in Bădică and Bădică (2016).…”

Section: Real Robotic Implementation Of Foraging Agentsmentioning

confidence: 99%

Multi-Agent Foraging: state-of-the-art and research challenges

Zedadra

Jouandeau²,

Séridi

et al. 2017

Complex Adapt Syst Model

Self Cite

View full text Add to dashboard Cite

BackgroundSwarm intelligence provides the design and implementation of systems composed of many simple individuals who interact locally and produce remarkable behavior as a whole (Dudek et al. 1996). It provides multiple benefits such as robustness where the performance of the system is not affected significantly with the failure of individuals, simplicity of computational and perceptual capabilities of individuals but still allowing global complex behaviors and scalability of the control mechanism that does not depend on the number of agents (Mitton and Simplot-Ryl 2014). The application of swarm intelligence to collective robotics is identified as Swarm Robotics in El Zoghby et al. (2014). Many artificial systems such as distributed computing systems and artificial intelligence systems are characterized by complex behaviors that emerge as a result of the nonlinear spatio-temporal interactions among a large number of system components at different levels of organization. These systems are known as Complex Adaptive Systems (CAS) as stated by Lansing (2003). Holland (2006) also considers CAS as dynamic systems able to adapt in and evolve with a changing environment. MAF problem is a benchmark problem for swarm robotics. It can be seen as a CAS and defined like in Niazi and Hussain Abstract Background: The foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth.Results: First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems Conclusions:Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic. Zedadra et al. Complex Adapt Syst Model (2017) et al. Complex Adapt Syst Model (2017) 5:3 (2012) as a system made up of multiple simple individuals which interact in a nonlinear fashion, thereby giving rise to global and often unpredictable behaviors. A good way to understand a CAS is to study them in special cases, thus to simulate dedicated behavior from particular perspectives. Holland (2006) states that the analysis of CAS is done through a combination of applied, theoretical and experimental methods (e.g. mathematics and computer simulations). Authors in Fortino and North (2013) state that Agent-Based Modeling (ABM) has proven to provide an effective set of tools for modeling and simulating different ...

show abstract

Section: Real Robotic Implementation Of Foraging Agentsmentioning

confidence: 99%

Multi-Agent Foraging: state-of-the-art and research challenges

Zedadra

Jouandeau²,

Séridi

et al. 2017

Complex Adapt Syst Model

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among the works not considered in Section 3, at least the following three are worth to be mentioned here: the SADE (Dong et al, 2009) and ELDA (Fortino et al, 2010) development frameworks, as well as the iCore European project (Vlacheas et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…In ELDA (Fortino et al, 2010), the event abstraction is adopted to design a lightweight agent model-indeed, ELDA. In particular, any ELDA agent is a single-threaded autonomous entity interacting through asynchronous events, whose behaviour is expressed reactively in response to incoming events.…”

Section: Related Workmentioning

confidence: 99%

Coordinating activities and change: An event-driven architecture for situated MAS

Mariani

Omicini

2015

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

“…However, differently from object-oriented programming, where a unique reference object model exists, many different agent programming models have been to date proposed. Among them, two main models for the definition of agent behaviors emerged respectively from the Artificial Intelligence and the Distributed Systems communities: Belief-DesireIntention (BDI) (Rao and Georgeff, 1995) and StateChart-oriented (SC) (Fortino et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the SC model considers the behaviour of an agent as a set of states, while events occurring in the environment, and/or in the agent itself, drive transitions from a state to another, also triggering proper actions (Harel, 1987;Fortino et al, 2010;Griss et al, 2010).…”

Section: Introductionmentioning

confidence: 99%