Quantitative analysis of multi-agent systems through statistical verification of simulation traces

Herd, Benjamin; Miles, Simon; McBurney, Peter; Lück, Michael

doi:10.1504/ijaose.2018.094373

Cited by 5 publications

(4 citation statements)

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“…It is challenging to determine whether a conclusion drawn from an agent-based model, such as whether a particular behaviour is likely to emerge, is robust or just reflects random chance or the absence of some critical features of the simulated phenomena. Miles and colleagues have developed methods and tools for quantifying whether an observed property can confidently be asserted about the modelled system, drawing on approximate probabilistic model checking and temporal logic [45]. They extended this general approach to allow causal processes within a model to be detected [44].…”

Section: Main Approaches and Key Resultsmentioning

confidence: 99%

Reasoning and interaction for social artificial intelligence

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Cocarascu

et al. 2022

AIC

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Current work on multi-agent systems at King’s College London is extensive, though largely based in two research groups within the Department of Informatics: the Distributed Artificial Intelligence (DAI) thematic group and the Reasoning & Planning (RAP) thematic group. DAI combines AI expertise with political and economic theories and data, to explore social and technological contexts of interacting intelligent entities. It develops computational models for analysing social, political and economic phenomena to improve the effectiveness and fairness of policies and regulations, and combines intelligent agent systems, software engineering, norms, trust and reputation, agent-based simulation, communication and provenance of data, knowledge engineering, crowd computing and semantic technologies, and algorithmic game theory and computational social choice, to address problems arising in autonomous systems, financial markets, privacy and security, urban living and health. RAP conducts research in symbolic models for reasoning involving argumentation, knowledge representation, planning, and other related areas, including development of logical models of argumentation-based reasoning and decision-making, and their usage for explainable AI and integration of machine and human reasoning, as well as combining planning and argumentation methodologies for strategic argumentation.

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Section: Main Approaches and Key Resultsmentioning

confidence: 99%

Reasoning and interaction for social artificial intelligence

Black

Brandão

Cocarascu

et al. 2022

AIC

Self Cite

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“…This is impossible where simulations are manually developed from domain models. In generating simulation experiment models, the automated generator can take into consideration expected boundaries for statistical significance and choose appropriate sensitivity analyses for robustness checking—for example by building on tools such as [ 20 ] or Spartan [ 5 ]. Again, the generation rules are explicit artefacts that can be referenced from the fitness-for-purpose argument and inspected as needed.…”

Section: Overview Of Visionmentioning

confidence: 99%

“…For example, Spartan [ 5 ] is a toolkit supporting statistical analysis of simulation runs to alleviate aleatory uncertainty and undertake sensitivity analysis, focusing on numerical output data. Similarly, [ 20 ] provides support for drawing statistically sound conclusions based on temporal-logic queries about patterns of agent behaviour. However, neither of these approaches are currently integrated with the initial domain model; they remain at the platform-specific level.…”

Section: Related Workmentioning

confidence: 99%

Trustworthy agent-based simulation: the case for domain-specific modelling languages

Zschaler

Polack

2023

Softw Syst Model

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Simulation is a key tool for researching complex system behaviour. Agent-based simulation has been applied across domains, such as biology, health, economics and urban sciences. However, engineering robust, efficient, maintainable, and reliable agent-based simulations is challenging. We present a vision for engineering agent simulations comprising a family of domain-specific modelling languages (DSMLs) that integrates core software engineering, validation and simulation experimentation. We relate the vision to examples of principled simulation, to show how the DSMLs would improve robustness, efficiency, and maintainability of simulations. Focusing on how to demonstrate the fitness for purpose of a simulator, the envisaged approach supports bi-directional transparency and traceability between the original domain understanding to the implementation, interpretation of results and evaluation of hypotheses.

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“…On the first day of the workshop, participants presented their perspectives on the state of the art in simulation and simulation tools, introducing their existing work. On the application side, this included work using simulation in immunology (e.g., [1,7,11,13,15,17]), vascular biology (e.g., [4,5]), and synthetic biology [12], as well as in the social sciences [8] and, more generally, the evaluation of simulation results (e.g., [9,10]). Equally, Cosmo Tech, Slingshot Simulations, and the FLAME GPU Team presented on different approaches to high-performance simulation platforms that allow for domain specialization.…”

Section: Overview Of Workhop and Participantsmentioning

confidence: 99%