“Sometimes” and “not never” revisited: on branching versus linear time temporal logic
Journal of the ACM (JACM) - The MIT Press scientific computation series
Avoiding the state explosion problem in temporal logic model checking
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Symbolic model checking: 1020 states and beyond
Information and Computation - Special issue: Selections from 1990 IEEE symposium on logic in computer science
UPPAAL—a tool suite for automatic verification of real-time systems
Proceedings of the DIMACS/SYCON workshop on Hybrid systems III : verification and control: verification and control
Model checking
NUSMV: A New Symbolic Model Verifier
CAV '99 Proceedings of the 11th International Conference on Computer Aided Verification
Minimalist coherent swarming of wireless networked autonomous mobile robots
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
ASE '00 Proceedings of the 15th IEEE international conference on Automated software engineering
Constraint-Based Verification of Parameterized Cache Coherence Protocols
Formal Methods in System Design
Verification of NASA Emergent Systems
ICECCS '04 Proceedings of the Ninth IEEE International Conference on Engineering Complex Computer Systems Navigating Complexity in the e-Engineering Age
A formal analysis of bluetooth device discovery
International Journal on Software Tools for Technology Transfer (STTT)
Spin model checker, the: primer and reference manual
Spin model checker, the: primer and reference manual
Using probabilistic model checking and simulation for designing self-organizing systems
Proceedings of the 2009 ACM symposium on Applied Computing
Strategies for energy optimisation in a swarm of foraging robots
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
Space-Time Continuous Models of Swarm Robotic Systems: Supporting Global-to-Local Programming
Space-Time Continuous Models of Swarm Robotic Systems: Supporting Global-to-Local Programming
Formal verification of probabilistic swarm behaviours
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Towards temporal verification of emergent behaviours in swarm robotic systems
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Formal verification and simulation for performance analysis for probabilistic broadcast protocols
ADHOC-NOW'06 Proceedings of the 5th international conference on Ad-Hoc, Mobile, and Wireless Networks
From swarm intelligence to swarm robotics
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
A review of probabilistic macroscopic models for swarm robotic systems
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
PRISM: a tool for automatic verification of probabilistic systems
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Temporal Logic Planning and Control of Robotic Swarms by Hierarchical Abstractions
IEEE Transactions on Robotics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Towards temporal verification of swarm robotic systems
Robotics and Autonomous Systems
Analysing robot swarm decision-making with Bio-PEPA
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Formal modeling of robot behavior with learning
Neural Computation
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An alternative to deploying a single robot of high complexity can be to utilise robot swarms comprising large numbers of identical, and much simpler, robots. Such swarms have been shown to be adaptable, fault-tolerant and widely applicable. However, designing individual robot algorithms to ensure effective and correct overall swarm behaviour is actually very difficult. While mechanisms for assessing the effectiveness of any swarm algorithm before deployment are essential, such mechanisms have traditionally involved either computational simulations of swarm behaviour, or experiments with robot swarms themselves. However, such simulations or experiments cannot, by their nature, analyse all possible swarm behaviours. In this paper, we will develop and apply the use of automated probabilistic formal verification techniques to robot swarms, involving an exhaustive mathematical analysis, in order to assess whether swarms will indeed behave as required. In particular we consider a foraging robot scenario to which we apply probabilistic model checking.