Modeling and verifying probabilistic Multi-Agent Systems using knowledge and social commitments

Sultan, Khalid; Wang, Wei; Al-Saqqar, Faisal

doi:10.1016/j.eswa.2014.04.008

Cited by 10 publications

(10 citation statements)

References 34 publications

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“…1 In these approaches, the assumption that “each agent can read each other mind” is given. As pointed out by Sultan, 18 though, mental-based models do not favor verification. A different approach is based on communications using publicly available information.…”

Section: Background On Knowledge and Commitments In Multi-agent Symentioning

confidence: 94%

“…Commitments are naturally related to interactions/communications. 4,18 From this perspective, two main approaches can be identified. In the so-called mentalistic approach, communications are in terms of privately defined information of agents, like the agent mental states of beliefs, desires , and intentions (the BDI metaphor).…”

Section: Background On Knowledge and Commitments In Multi-agent Symentioning

confidence: 99%

“…A critical issue when modeling social commitments is agent behavior uncertainty . Uncertainty, that is, non-deterministic or probabilistic behavior, 18 influences the agent evolution and makes it challenging to analyze the agent model.…”

Section: Background On Knowledge and Commitments In Multi-agent Symentioning

confidence: 99%

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Formal reasoning on knowledge and commitments in multi-agent systems using Theatre

Nigro

Sciammarella

2019

SIMULATION

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This paper proposes a formal method based on the Theatre framework for modeling and analysis of knowledge and commitments in multi-agent systems (MASs). Theatre centers on actors and a reduction on to Uppaal, which enables both non-deterministic analysis (that is, qualitative analysis by exhaustive verification, or showing that something can possibly occur) and quantitative analysis (that is, estimating probability measures of event occurrence through simulations) of the same model. The article describes the modeling and analysis approach based on Theatre and Uppaal, and shows its application to the modeling and property checking of the NetBill protocol used in web-based MAS applications when selling/buying goods. Properties of the NetBill protocol are demonstrated by experimental results.

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Section: Background On Knowledge and Commitments In Multi-agent Symentioning

confidence: 94%

Section: Background On Knowledge and Commitments In Multi-agent Symentioning

confidence: 99%

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Formal reasoning on knowledge and commitments in multi-agent systems using Theatre

Nigro

Sciammarella

2019

SIMULATION

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“…Finally, probabilistic models are not considered in the previous work. The exception is the work by Sultan et al (2014), which uses a probabilistic variant of CTL. However, Sultan et al only consider active commitments.…”

Section: Related Workmentioning

confidence: 99%

“…They also extend the standard symbolic model checking algorithm of CTL in line with their proposal. Sultan, Bentahar, Wan, and Al-Saqqar (2014) propose PCTLC, which extends probabilistic CTL, to verify commitment protocols. PCTLC includes social operators to represent active commitments and their fulfillment.…”

Section: Related Workmentioning

confidence: 99%

ProMoca: Probabilistic Modeling and Analysis of Agents in Commitment Protocols

Günay¹,

Liu²,

Zhang³

2016

jair

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Social commitment protocols regulate interactions of agents in multiagent systems. Several methods have been developed to analyze properties of commitment protocols. However, analysis of an agent's behavior in a commitment protocol, which should take into account the agent's goals and beliefs, has received less attention. In this paper we present ProMoca framework to address this issue. Firstly, we develop an expressive formal language to model agents with respect to their commitments. Our language provides dedicated elements to define commitment protocols, and model agents in terms of their goals, behaviors, and beliefs. Furthermore, our language provides probabilistic and non-deterministic elements to model uncertainty in agents' beliefs. Secondly, we identify two essential properties of an agent with respect to a commitment protocol, namely compliance and goal satisfaction. We formalize these properties using a probabilistic variant of linear temporal logic. Thirdly, we adapt a probabilistic model checking algorithm to automatically analyze compliance and goal satisfaction properties. Finally, we present empirical results about efficiency and scalability of ProMoca.

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