The best-of-n problem with dynamic site qualities: achieving adaptability with stubborn individuals

Conference paper


Prasetyo, J., De Masi, G., Ranjan, P. and Ferrante, E. 2018. The best-of-n problem with dynamic site qualities: achieving adaptability with stubborn individuals. ANTS 2018: 11th International Conference on Swarm Intelligence. Rome, Italy 29 - 31 Oct 2018 Springer. pp. 239-251 https://doi.org/10.1007/978-3-030-00533-7_19
TypeConference paper
TitleThe best-of-n problem with dynamic site qualities: achieving adaptability with stubborn individuals
AuthorsPrasetyo, J., De Masi, G., Ranjan, P. and Ferrante, E.
Abstract

Collective decision-making is one of main building blocks of swarm robotics collective behaviors. It is the ability of individuals to make a collective decision without any centralized leadership, but only via local interaction and communication. The best-of-n problem is a subclass of collective decision-making, whereby the swarm has to select the best option among a set of n possible alternatives. Recently, the best-of-n problems has gathered momentum: a number of decision-making mechanisms have been studied focusing both on cases where there is an explicit measurable difference between the two qualities, as well as on cases when there are only delay costs in the environment driving the consensus to one of the n alternatives. To the best of our knowledge, all the formal studies on the best-of-n problem have considered a site quality distribution that is stationary and does not change over time.

In this paper, we perform a study of the best-of-n problems in a dynamic environment setting. We consider the situation where site qualities can be directly measured by agents, and we introduce abrupt changes to these qualities, whereby the two qualities are swapped at a given time.

Using computer simulations, we show that a vanilla application of one of the most studied decision-making mechanism, the voter model, does not guarantee adaptation of the swarm consensus towards the best option after the swap occurs. Therefore, we introduce the notion of stubborn agents, which are not allowed to change their opinion. We show that the presence of the stubborn agents is enough to achieve adaptability to dynamic environments. We study the performance of the system with respect to a number of key parameters: the swarm size, the difference between the two qualities and the proportion of stubborn individuals.

Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
ConferenceANTS 2018: 11th International Conference on Swarm Intelligence
Page range239-251
Proceedings TitleSwarm Intelligence: 11th International Conference, ANTS 2018, Rome, Italy, October 29–31, 2018, Proceedings
SeriesLecture Notes in Computer Science
ISSN0302-9743
Electronic1611-3349
ISBN
Paperback9783030005320
Electronic9783030005337
PublisherSpringer
Publication dates
Print03 Oct 2018
Online03 Oct 2018
Publication process dates
Submitted15 Apr 2018
Accepted15 Jun 2018
Deposited30 Jan 2025
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-00533-7_19
Scopus EID2-s2.0-85055795080
Web of Science identifierWOS:000672741400019
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