Collective consensus forming in spatially distributed systems is a challenging task. In previous literature, multi-option consensus-forming scenarios, with the number of options being smaller or equal to the number of agents, have been well studied. However, many well-performing decision-making strategies on a few options suffer from scalability when the number of options increases, especially for many-option scenarios with significantly more options than agents. In this paper, we investigate the viabilities of discrete decision-making strategies with ranked voting (RV) and belief fusion (DBBS) decision mechanisms in many-option scenarios with large decision spaces compared to the number of agents. We test the investigated strategies on an expanded discrete collective estimation scenario where the decision space can be expanded using two factors: a higher number of environmental features and/or finer decision space discretization. We have used a continuous collective consensus forming strategy, linear consensus protocol (LCP), as a baseline. Our experimental results have shown that, although susceptible to environmental influences, discrete decision-making strategies can reliably outperform those of LCP in terms of error and convergence time at the tested sizes of decision space. We have also shown that the two factors that lead to the expansion of the decision space have different impacts on performances for both RV and DBBS strategies, due to differences in the correlations between the discrete options. When facing a higher number of features, both discrete strategies experience a smaller error and a significant increase in decision time, while a finer decision space discretization has a negative influence on all considered metrics.

AbstractSocial learning is a collective approach to decentralised decision-making and is comprised of two processes; evidence updating and belief fusion. In this paper we propose a social learning model in which agents’ beliefs are represented by a set of possible states, and where the evidence collected can vary in its level of imprecision. We investigate this model using multi-agent and multi-robot simulations and demonstrate that it is robust to imprecise evidence. Our results also show that certain kinds of imprecise evidence can enhance the efficacy of the learning process in the presence of sensor errors.

Colonies of ants can complete complex tasks without the need for centralised control as a result of interactions between individuals and their environment. Particularly remarkable is the process of path selection between the nest and food sources that is essential for successful foraging. We have designed a stochastic model of ant foraging in the absence of direct communication. The motion of ants is governed by two components - a random change in direction of motion that improves ability to explore the environment, and a non-random global indirect interaction component based on pheromone signalling. Our model couples individual-based off-lattice ant simulations with an on-lattice characterisation of the pheromone diffusion. Using numerical simulations we have tested three pheromone-based model alternatives: (1) a single pheromone laid on the way toward the food source and on the way back to the nest; (2) single pheromone laid on the way toward the food source and an internal imperfect compass to navigate toward the nest; (3) two different pheromones, each used for one direction. We have studied the model behaviour in different parameter regimes and tested the ability of our simulated ants to form trails and adapt to environmental changes. The simulated ants behaviour reproduced the behaviours observed experimentally. Furthermore we tested two biological hypotheses on the impact of the quality of the food source on the dynamics. We found that increasing pheromone deposition for the richer food sources has a larger impact on the dynamics than elevation of the ant recruitment level for the richer food sources.

An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain simplicity in their decision models, whilst increasing the swarm’s abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm control intelligent agent (shepherd). We first use swarm metrics to recognise the type of swarm that the shepherd interacts with, then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. contents) of the control agent without sacrificing the low computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms.