Robust distributed decision-making in robot swarms: Exploiting a third truth state

Crosscombe, Michael; Lawry, Jonathan; Hauert, Sabine; Homer, Martin

doi:10.1109/iros.2017.8206297

Cited by 21 publications

(22 citation statements)

References 25 publications

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“…In this context it is shown that combining updating and pooling leads to faster convergence and better consensus than Bayesian updating alone. An alternative methodology exploits three-valued logic to combine both types of evidence [Crosscombe and Lawry, 2016] and has been effectively applied to distributed decision-making in swarm robotics [Crosscombe et al, 2017].…”

Section: Introductionmentioning

confidence: 99%

Evidence Propagation and Consensus Formation in Noisy Environments

Crosscombe

Lawry

2019

Lecture Notes in Computer Science

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We study the effectiveness of consensus formation in multi-agent systems where there is both belief updating based on direct evidence and also belief combination between agents. In particular, we consider the scenario in which a population of agents collaborate on the best-of-n problem where the aim is to reach a consensus about which is the best (alternatively, true) state from amongst a set of states, each with a different quality value (or level of evidence). Agents' beliefs are represented within Dempster-Shafer theory by mass functions and we invegate the macro-level properties of four wellknown belief combination operators for this multiagent consensus formation problem: Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging operator. The convergence properties of the operators are considered and simulation experiments are conducted for different evidence rates and noise levels. Results show that a combination of updating from direct evidence and belief combination between agents results in better consensus to the best state than does evidence updating alone. We also find that in this framework the operators are robust to noise. Broadly, Dubois & Prade's operator results in better convergence to the best state. Finally, we consider how well the Dempster-Shafer approach to the best-of-n problem scales to large numbers of states.

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Section: Introductionmentioning

confidence: 99%

Evidence Propagation and Consensus Formation in Noisy Environments

Crosscombe

Lawry

2019

Lecture Notes in Computer Science

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“…As the research community continues to make the case for applying swarm robotics to real-world problems, it is important to identify whether the proposed solutions can perform well in complex environments. Previously it has been shown that both the robustness and scalability of current approaches suffer in the presence of noisy and complex environments, for example in the weighted voter model (Crosscombe et al 2017), with few models since having attempted to address these problems (Lawry et al 2019;Lee et al 2018b). So far we have shown that using our approach agents can learn an accurate ordering of n = 10 options and that the model is robust to high levels of noise under certain conditions, i.e.…”

Section: Scalability Of Collective Preference Learningmentioning

confidence: 85%

“…This limitation is often due to their having been inspired by, or even attempting to model directly, the solutions found in natural systems such as the nest site selection behaviours of social insects (Seeley and Buhrman 2001;Britton et al 2002;Sumpter and Pratt 2009). Recently, however, we have seen increased interest in developing models that move us closer to the goal of deploying robot swarms for real-world applications, for example by proposing models for collective learning which consider more complex environments as well as their robustness to the presence of noise or error (Crosscombe et al 2017;Lee et al 2018b).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Collective preference learning in the best-of-n problem

Crosscombe

Lawry

2021

Swarm Intell

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Decentralised autonomous systems rely on distributed learning to make decisions and to collaborate in pursuit of a shared objective. For example, in swarm robotics the best-of-n problem is a well-known collective decision-making problem in which agents attempt to learn the best option out of n possible alternatives based on local feedback from the environment. This typically involves gathering information about all n alternatives while then systematically discarding information about all but the best option. However, for applications such as search and rescue in which learning the ranking of options is useful or crucial, best-of-n decision-making can be wasteful and costly. Instead, we investigate a more general distributed learning process in which agents learn a preference ordering over all of the n options. More specifically, we introduce a distributed rank learning algorithm based on three-valued logic. We then use agent-based simulation experiments to demonstrate the effectiveness of this model. In this context, we show that a population of agents are able to learn a total ordering over the n options and furthermore the learning process is robust to evidential noise. To demonstrate the practicality of our model, we restrict the communication bandwidth between the agents and show that this model is also robust to limited communications whilst outperforming a comparable probabilistic model under the same communication conditions.

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“…Using the AR DOME, one could already implement decision making algorithms [30], synchronised signaling [31], or patterning [32]. In the future, we aim to provide other modes of augmented communication, including depositing information (in this case light) in the environment (i.e.…”

Section: Future Workmentioning

confidence: 99%

Augmented reality for the engineering of collective behaviours in microsystems

Denniss

Gorochowski

Hauert

2019

2019 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

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Microsystems composed of responsive particles or even living cells often operate in large numbers to perform tasks beyond the capabilities of each individual. Engineering collective behaviours in such systems could lead to breakthroughs in medicine, or entirely new applications. Key to many collective behaviours is the ability for micro-agents to communicate with their neighbours through chemical signaling, energy transfer (e.g. heat), or modification of the environment (stigmergy). However, implementing such communication modalities is typically challenging and time consuming. To simplify this process, we propose to use augmented reality (AR) to implement a new form of communication channel using light. Our AR Dynamic Optical Micro-Environment (AR Dome) is able to provide micro-agents with varying communication ranges and the ability to modify their environment in complex ways. AR is achieved through a custom made closed-loop system based on an ultra violet (UV) light projector and imaging apparatus. Using AR DOME, we show how micro-particles can be endowed with new communication capabilities and demonstrate propagation of an AR-based light communication signal through a population of micro-particles.

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Robust distributed decision-making in robot swarms: Exploiting a third truth state

Cited by 21 publications

References 25 publications

Evidence Propagation and Consensus Formation in Noisy Environments

Evidence Propagation and Consensus Formation in Noisy Environments

Collective preference learning in the best-of-n problem

Augmented reality for the engineering of collective behaviours in microsystems

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