263,567 research outputs found
Modularity and Openness in Modeling Multi-Agent Systems
We revisit the formalism of modular interpreted systems (MIS) which
encourages modular and open modeling of synchronous multi-agent systems. The
original formulation of MIS did not live entirely up to its promise. In this
paper, we propose how to improve modularity and openness of MIS by changing the
structure of interference functions. These relatively small changes allow for
surprisingly high flexibility when modeling actual multi-agent systems. We
demonstrate this on two well-known examples, namely the trains, tunnel and
controller, and the dining cryptographers.
Perhaps more importantly, we propose how the notions of multi-agency and
openness, crucial for multi-agent systems, can be precisely defined based on
their MIS representations.Comment: In Proceedings GandALF 2013, arXiv:1307.416
Evoplex: A platform for agent-based modeling on networks
Agent-based modeling and network science have been used extensively to
advance our understanding of emergent collective behavior in systems that are
composed of a large number of simple interacting individuals or agents. With
the increasing availability of high computational power in affordable personal
computers, dedicated efforts to develop multi-threaded, scalable and
easy-to-use software for agent-based simulations are needed more than ever.
Evoplex meets this need by providing a fast, robust and extensible platform for
developing agent-based models and multi-agent systems on networks. Each agent
is represented as a node and interacts with its neighbors, as defined by the
network structure. Evoplex is ideal for modeling complex systems, for example
in evolutionary game theory and computational social science. In Evoplex, the
models are not coupled to the execution parameters or the visualization tools,
and there is a user-friendly graphical interface which makes it easy for all
users, ranging from newcomers to experienced, to create, analyze, replicate and
reproduce the experiments.Comment: 6 pages, 5 figures; accepted for publication in SoftwareX [software
available at https://evoplex.org
Intelligent agent for formal modelling of temporal multi-agent systems
Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system
DYNASTAT: A Methodology for Dynamic and Static Modeling of Multi-agent Systems
Multi-agent systems are increasingly being used within various knowledge domains. The need for modeling of the multi-agent systems in a systematic and effective way is becoming more evident. In this chapter, we present the DYNASTAT methodology. This methodology involves a conceptual overview of multi-agent systems, a selection of specific agent characteristics to model, and a discussion of what has to be modeled for each of these agent characteristics. DYNASTAT is independent of any particular modeling language but provides a framework that can be used to realize a particular language in the context of a real-world example. UML 2.2 was chosen as the modeling language to implement the DYNASTAT methodology and this was illustrated using examples from the medical domain. Several UML 2.2 diagrams were selected including a use case, composite structure, sequence and activity diagram to model a multi-agent system able to assist botha medical researcher and a primary care physician. UML 2.2 provides a framework for effective modeling of agent-based systems in a standardized way which this chapter endeavors to demonstrate
Specification and Verification of Commitment-Regulated Data-Aware Multiagent Systems
In this paper we investigate multi agent systems whose agent interaction is based on social commitments that evolve over time, in presence of (possibly incomplete) data. In particular, we are interested in modeling and verifying how data maintained by the agents impact on the dynamics of such systems, and on the evolution of their commitments. This requires to lift the commitment-related conditions studied in the literature, which are typically based on propositional logics, to a first-order setting. To this purpose, we propose a rich framework for modeling data-aware commitment-based multiagent systems. In this framework, we study verification of rich temporal properties, establishing its decidability under the condition of “state-boundedness”, i.e., data items come from an infinite domain but, at every time point, each agent can store only a bounded number of them
Use of UML 2.1 to model multi-agent systems based on a goal-driven software engineering ontology
In this paper, we present the use of UML 2.1 to model multi-agent systems based on a goal-driven software engineering ontology. The lack of an efficient standardized modeling language is evident. The uses of UML and stereotypes UML to model multi-agent systems have been proposed. However, there are still a number of issues with the existing approaches due to inconsistent semantics of the existing UML diagrams and unintuitive and complext notations. UML 2.1 allows representing more complex scenarios and introducing greater details into the modeling process enabling effective capture and representation of multi-agent actions and interactions. UML 2.1 has not only enabled the introduction of a notation for the Ontology based multi-agent systems, but also effective capture and representation of the dynamic processes associated with these Ontology based multi-agent systems
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