Design patterns for multi-agent programming

Dastani, Mehdi; Testerink, Bas

doi:10.1504/ijaose.2016.10001872

Cited by 4 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We introduce a novel agent programming language that we call Sim-2APL . Sim-2APL is an adaptation of 2APL, 36 a Java library for the implementation of autonomous software agents, i.e., software agents that sense their environment, decide their plans of actions, and execute those plans.…”

Section: Sim-2aplmentioning

confidence: 99%

“…2APL is a Java library for implementing autonomous software agents using object-oriented design patterns (see Dastani and Testerink 36 and Dastani 87 for technical details). A 2APL agent is programmed through four major components.…”

Section: Sim-2aplmentioning

confidence: 99%

“…As the agents can dynamically update their belief, goal, and plan bases, 2APL allows the implementation of many forms of complex proactive and reactive behavior. 36 However, 2APL has two main limitations that make it unsuitable for simulation.…”

Section: Sim-2aplmentioning

confidence: 99%

“…The proposed framework supports simulations with millions of agents that can individually deliberate about their own knowledge, goals, and preferences, and can adapt their behavior during the simulation based on observations of other agents’ behaviors and on their (possibly evolving) attitudes and intentions to comply with norms. To implement deliberative agents, we propose a novel adaptation of A Practical Agent Programming Language (2APL) 36 —a well-known Java-based programming language that is designed to support the implementation of autonomous agents—which we call Sim-2APL . In addition, we propose PanSim , a novel epidemic simulation platform that allows the execution of large-scale agent-based epidemic simulations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A framework for modeling human behavior in large-scale agent-based epidemic simulations

et al. 2023

View full text Add to dashboard Cite

Agent-based modeling is increasingly being used in computational epidemiology to characterize important behavioral dimensions, such as the heterogeneity of the individual responses to interventions, when studying the spread of a disease. Existing agent-based simulation frameworks and platforms currently fall in one of two categories: those that can simulate millions of individuals with simple behaviors (e.g., based on simple state machines), and those that consider more complex and social behaviors (e.g., agents that act according to their own agenda and preferences, and deliberate about norm compliance) but, due to the computational complexity of reasoning involved, have limited scalability. In this paper, we present a novel framework that enables large-scale distributed epidemic simulations with complex behaving social agents whose decisions are based on a variety of concepts and internal attitudes such as sense, knowledge, preferences, norms, and plans. The proposed framework supports simulations with millions of such agents that can individually deliberate about their own knowledge, goals, and preferences, and can adapt their behavior based on other agents’ behaviors and on their attitude toward complying with norms. We showcase the applicability and scalability of the proposed framework by developing a model of the spread of COVID-19 in the US state of Virginia. Results illustrate that the framework can be effectively employed to simulate disease spreading with millions of complex behaving agents and investigate behavioral interventions over a period of time of months.

show abstract

Section: Sim-2aplmentioning

confidence: 99%

Section: Sim-2aplmentioning

confidence: 99%

Section: Sim-2aplmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A framework for modeling human behavior in large-scale agent-based epidemic simulations

et al. 2023

View full text Add to dashboard Cite

show abstract

“…An important sub-field of autonomous systems is multi-agent systems (Wooldrige, 2009) that studies the operation and coordination of autonomous agents that aim at solving problems that go beyond the capabilities of single agents. Different authors have presented patterns that document problem-solution pairs for engineering multi-agent systems (Dastani and Testerink, 2016;Marks, Muller, Vogeli, Jung, Jazdi, and Weyrich, 2018;Schelfthout, Coninx, Helleboogh, Holvoet, Steegmans, and Weyns, 2002). Juziuk, Weyns, and Holvoet (2014) presented a systematic literature overview classifying patterns based on focus, granularity, level of abstraction, and source of inspiration.…”

Section: Smart Systemsmentioning

confidence: 99%

The vision of self-evolving computing systems

Weyns

Bäck

Vidal

et al. 2023

JID

View full text Add to dashboard Cite

Computing systems are omnipresent; their sustainability has become crucial for our society. A key aspect of this sustainability is the ability of computing systems to cope with the continuous change they face, ranging from dynamic operating conditions, to changing goals, and technological progress. While we are able to engineer smart computing systems that autonomously deal with various types of changes, handling unanticipated changes requires system evolution, which remains in essence a human-centered process. This will eventually become unmanageable. To break through the status quo, we put forward an arguable opinion for the vision of self-evolving computing systems that are equipped with an evolutionary engine enabling them to evolve autonomously. Specifically, when a self-evolving computing systems detects conditions outside its operational domain, such as an anomaly or a new goal, it activates an evolutionary engine that runs online experiments to determine how the system needs to evolve to deal with the changes, thereby evolving its architecture. During this process the engine can integrate new computing elements that are provided by computing warehouses. These computing elements provide specifications and procedures enabling their automatic integration. We motivate the need for self-evolving computing systems in light of the state of the art, outline a conceptual architecture of self-evolving computing systems, and illustrate the architecture for a future smart city mobility system that needs to evolve continuously with changing conditions. To conclude, we highlight key research challenges to realize the vision of self-evolving computing systems.

show abstract

Quantifying the Effects of Norms on COVID-19 Cases Using an Agent-Based Simulation

Mooij

Dell’Anna

Bhattacharya

et al. 2022

Multi-Agent-Based Simulation XXII

View full text Add to dashboard Cite

Modelling social phenomena in large-scale agent-based simulations has long been a challenge due to the computational cost of incorporating agents whose behaviors are determined by reasoning about their internal attitudes and external factors. However, COVID-19 has brought the urgency of doing this to the fore, as, in the absence of viable pharmaceutical interventions, the progression of the pandemic has primarily been driven by behaviors and behavioral interventions. In this paper, we address this problem by developing a large-scale data-driven agent-based simulation model where individual agents reason about their beliefs, objectives, trust in government, and the norms imposed by the government. These internal and external attitudes are based on actual data concerning daily activities of individuals, their political orientation, and norms being enforced in the US state of Virginia. Our model is calibrated using mobility and COVID-19 case data. We show the utility of our model by quantifying the benefits of the various behavioral interventions through counterfactual runs of our calibrated simulation.

show abstract

Design patterns for multi-agent programming

Cited by 4 publications

References 0 publications

A framework for modeling human behavior in large-scale agent-based epidemic simulations

A framework for modeling human behavior in large-scale agent-based epidemic simulations

The vision of self-evolving computing systems

Quantifying the Effects of Norms on COVID-19 Cases Using an Agent-Based Simulation

Contact Info

Product

Resources

About