5 research outputs found
Evidence Propagation and Consensus Formation in Noisy Environments
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 investigate the macro-level properties of four well-known
belief combination operators for this multi-agent 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 on 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, Yager's rule is shown to be the better
operator under various parameter values, i.e. convergence to the best state,
robustness to noise, and scalability.Comment: 13th international conference on Scalable Uncertainty Managemen
The Impact of Network Connectivity on Collective Learning
In decentralised autonomous systems it is the interactions between individual
agents which govern the collective behaviours of the system. These local-level
interactions are themselves often governed by an underlying network structure.
These networks are particularly important for collective learning and
decision-making whereby agents must gather evidence from their environment and
propagate this information to other agents in the system. Models for collective
behaviours may often rely upon the assumption of total connectivity between
agents to provide effective information sharing within the system, but this
assumption may be ill-advised. In this paper we investigate the impact that the
underlying network has on performance in the context of collective learning.
Through simulations we study small-world networks with varying levels of
connectivity and randomness and conclude that totally-connected networks result
in higher average error when compared to networks with less connectivity.
Furthermore, we show that networks of high regularity outperform networks with
increasing levels of random connectivity.Comment: 13 pages, 5 figures. To appear at the 15th International Symposium on
Distributed Autonomous Robotic Systems 2021. Presented at the joint
DARS-SWARM 2021 symposium held (virtually) in Kyoto, Japa
Negative updating applied to the best-of-n problem with noisy qualities
The ability to perform well in the presence of noise is an important consideration when evaluating the effectiveness of a collective decision-making framework. Any system deployed for real-world applications will have to perform well in complex and uncertain environments, and a component of this is the limited reliability and accuracy of evidence sources. In particular, in swarm robotics there is an emphasis on small and inexpensive robots which are often equipped with low-cost sensors more prone to suffer from noisy readings. This paper presents an exploratory investigation into the robustness of a negative updating approach to the best-of-n problem which utilises negative feedback from direct pairwise comparison of options and opinion pooling. A site selection task is conducted with a small-scale swarm of five e-puck robots choosing between n= 7 options in a semi-virtual environment with varying levels of sensor noise. Simulation experiments are then used to investigate the scalability of the approach. We now vary the swarm size and observe the behaviour as the number of options n increases for different error levels with different pooling regimes. Preliminary results suggest that the approach is robust to noise in the form of noisy sensor readings for even small populations by supporting self-correction within the population