Automated Verification of Resource Requirements in Multi-Agent Systems Using Abstraction

Alechina, Natasha; Logan, Brian; Nguyen, Hoang Nga; Rakib, Abdur

doi:10.1007/978-3-642-20674-0_5

Cited by 6 publications

(8 citation statements)

References 16 publications

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“…In this paper, we give a more detailed and technical explanation of the methodology and apply the approach to practically verify a variety of complex autonomous systems (see sections 3, 4, and 5). We argue that the most crucial aspect of verifying complex decision-making algorithms for autonomous systems, for example concerning safety, is to identify that the controlling agent never deliberately makes a choice it believes to be unsafe 1 . In particular this is important in answering questions about whether a decision-making agent will make the same decisions as a human operator given the same information from its sensors.…”

Section: A Methodology For Verifying Autonomous Choicesmentioning

confidence: 99%

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Practical verification of decision-making in agent-based autonomous systems

et al. 2014

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We present a verification methodology for analysing the decision-making component in agent-based hybrid systems. Traditionally hybrid automata have been used to both implement and verify such systems, but hybrid automata based modelling, programming and verification techniques scale poorly as the complexity of discrete decision-making increases making them unattractive in situations where complex logical reasoning is required. In the programming of complex systems it has, therefore, become common to separate out logical decision-making into a separate, discrete, component. However, verification techniques have failed to keep pace with this development. We are exploring agent-based logical components and have developed a model checking technique for such components which can then be composed with a separate analysis of the continuous part of the hybrid system. Among other things this allows program model checkers to be used to verify the actual implementation of the decision-making in hybrid autonomous systems.

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Section: A Methodology For Verifying Autonomous Choicesmentioning

confidence: 99%

“…There are a number of approaches to model checking and verifying multi-agent systems [1,63,42]. Most of these are not specifically geared towards BDI-style rational agents but provide more general tools for the analysis of agents.…”

Section: Model Checking Agent Programsmentioning

confidence: 99%

Practical verification of decision-making in agent-based autonomous systems

et al. 2014

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“…When checking invariant (safety) properties, the model-checker will either determine that no states violate the invariant by exploring the entire state space, or will find a state violating the invariant and produce a counter-example. 4 However, even with state-of-the-art BDDbased model-checkers, memory exhaustion can occur when computing the reachable state space due to the large size of the intermediate BDDs (because of the high branching factor). The model checking performance based on depth-first search can also vary dramatically from good to worst.…”

Section: Managing Complexity Through Strategy and Abstractionmentioning

confidence: 99%

Model checking ontology‐driven reasoning agents using strategy and abstraction

Rakib

Faruqui

2019

Concurrency and Computation

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We present a framework for the modelling, specification and verification of ontology-driven multi-agent rule-based systems (MASs). We assume that each agent executes in a separate process and that they communicate via message passing. The proposed approach makes use of abstract specifications to model the behaviour of some of the agents in the system, and exploits information about the reasoning strategy adopted by the agents. Abstract specifications are given as Linear Temporal Logic (LTL) formulas which describe the external behaviour of the agents, allowing their temporal behaviour to be compactly modelled. Both abstraction and strategy have been combined in an automated model checking encoding tool TOVRBA for rule-based multi-agent systems which allows the system designer to specify information about agents' interaction, behaviour, and execution strategy at different levels of abstraction. The TOVRBA tool generates an encoding of the system for the Maude LTL model checker, allowing properties of the system to be verified.

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“…In [32], [33] authors have presented automated verification of resource requirements of reasoning agents using the Mocha model checker. In [34] the same authors presented preliminary work considering first order Horn clause rules and Maude LTL model checker, and illustrated the scalability of their approach by comparing it to results presented in [32]. In [35] authors presented framework to verify heterogeneous multi-agent programs based on meta-APL, where a heterogeneous multi-agent program is initially translated to meta-APL and then resulting system is verified using Maude.…”

Section: Related Workmentioning

confidence: 99%

Modeling and verifying context-aware non-monotonic reasoning agents

Rakib

Haque

2015

2015 ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE)

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Abstract-This paper complements our previous work on formal modeling of resource-bounded context-aware systems, which handle inconsistent context information using defeasible reasoning, by focusing on automated analysis and verification. A case study demonstrates how model checking techniques can be used to formally analyze quantitative and qualitative properties of a context-aware system based on message passing among agents. The behavior (semantics) of the system is modeled by a term rewriting system and the desired properties are expressed as LTL formulas. The Maude LTL model checker is used to perform automated analysis of the system and verify non-conflicting context information guarantees it provides.

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Automated Verification of Resource Requirements in Multi-Agent Systems Using Abstraction

Cited by 6 publications

References 16 publications

Practical verification of decision-making in agent-based autonomous systems

Practical verification of decision-making in agent-based autonomous systems

Model checking ontology‐driven reasoning agents using strategy and abstraction

Modeling and verifying context-aware non-monotonic reasoning agents

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