Exploiting Vagueness for Multi-Agent Consensus

Michael Crosscombe; Jonathan Lawry

doi:10.1007/978-981-10-2564-8_5

Exploiting Vagueness for Multi-Agent Consensus

Michael Crosscombe, Jonathan Lawry

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

6 Citations (Scopus)

253 Downloads (Pure)

Abstract

A framework for consensus modelling is introduced using Kleene’s three valued logic as a means to express vagueness in agents’ beliefs. Explicitly borderline cases are inherent to propositions involving vague concepts where sentences of a propositional language may be absolutely true, absolutely false or borderline. By exploiting these intermediate truth values, we can allow agents to adopt a more vague interpretation of underlying concepts in order to weaken their beliefs and reduce the levels of inconsistency, so as to achieve consensus. We consider a consensus combination operation which results in agents adopting the borderline truth value as a shared viewpoint if they are in direct conflict. Simulation experiments are presented which show that applying this operator to agents chosen at random (subject to a consistency threshold) from a population, with initially diverse opinions, results in convergence to a smaller set of more precise shared beliefs. Furthermore, if the choice of agents for combination is dependent on the payoff of their beliefs, this acting as a proxy for performance or usefulness, then the system converges to beliefs which, on average, have higher payoff.

Original language	English
Title of host publication	Multi-agent and Complex Systems
Subtitle of host publication	Post-proceedings of the 2nd International Workshop on Smart Simulation and Modelling for Computer Systems (SSMCS 2015)
Editors	Quan Bai, Fenghui Ren, Katsuhide Fujita, Minjie Zhang, Takayuki Ito
Publisher	Springer, Singapore
Pages	67-78
Number of pages	12
ISBN (Electronic)	9789811025648
ISBN (Print)	9789811025631
DOIs	https://doi.org/10.1007/978-981-10-2564-8_5
Publication status	Published - 16 Nov 2016
Event	2nd International Workshop on Smart Simulation and Modelling Computer Systems (SSMCS) - Buenos Aires, Argentina Duration: 25 Jul 2015 → 31 Jul 2015 Conference number: 2 http://www.uow.edu.au/~fren/SSMCS2015/

Publication series

Name	Studies in Computational Intelligence
Publisher	Springer
Volume	970
ISSN (Print)	1860-949X

Conference

Conference	2nd International Workshop on Smart Simulation and Modelling Computer Systems (SSMCS)
Abbreviated title	SSMCS
Country/Territory	Argentina
City	Buenos Aires
Period	25/07/15 → 31/07/15
Internet address	http://www.uow.edu.au/~fren/SSMCS2015/

Keywords

Agent-Based Modelling
Many-Valued Logics
Belief Aggregation
Consensus

Access to Document

10.1007/978-981-10-2564-8_5

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Crosscombe, M., & Lawry, J. (2016). Exploiting Vagueness for Multi-Agent Consensus. In Q. Bai, F. Ren, K. Fujita, M. Zhang, & T. Ito (Eds.), Multi-agent and Complex Systems: Post-proceedings of the 2nd International Workshop on Smart Simulation and Modelling for Computer Systems (SSMCS 2015) (pp. 67-78). (Studies in Computational Intelligence; Vol. 970). Springer, Singapore. https://doi.org/10.1007/978-981-10-2564-8_5

Crosscombe, Michael ; Lawry, Jonathan. / Exploiting Vagueness for Multi-Agent Consensus. Multi-agent and Complex Systems: Post-proceedings of the 2nd International Workshop on Smart Simulation and Modelling for Computer Systems (SSMCS 2015). editor / Quan Bai ; Fenghui Ren ; Katsuhide Fujita ; Minjie Zhang ; Takayuki Ito. Springer, Singapore, 2016. pp. 67-78 (Studies in Computational Intelligence).

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abstract = "A framework for consensus modelling is introduced using Kleene{\textquoteright}s three valued logic as a means to express vagueness in agents{\textquoteright} beliefs. Explicitly borderline cases are inherent to propositions involving vague concepts where sentences of a propositional language may be absolutely true, absolutely false or borderline. By exploiting these intermediate truth values, we can allow agents to adopt a more vague interpretation of underlying concepts in order to weaken their beliefs and reduce the levels of inconsistency, so as to achieve consensus. We consider a consensus combination operation which results in agents adopting the borderline truth value as a shared viewpoint if they are in direct conflict. Simulation experiments are presented which show that applying this operator to agents chosen at random (subject to a consistency threshold) from a population, with initially diverse opinions, results in convergence to a smaller set of more precise shared beliefs. Furthermore, if the choice of agents for combination is dependent on the payoff of their beliefs, this acting as a proxy for performance or usefulness, then the system converges to beliefs which, on average, have higher payoff.",

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Crosscombe, M & Lawry, J 2016, Exploiting Vagueness for Multi-Agent Consensus. in Q Bai, F Ren, K Fujita, M Zhang & T Ito (eds), Multi-agent and Complex Systems: Post-proceedings of the 2nd International Workshop on Smart Simulation and Modelling for Computer Systems (SSMCS 2015). Studies in Computational Intelligence, vol. 970, Springer, Singapore, pp. 67-78, 2nd International Workshop on Smart Simulation and Modelling Computer Systems (SSMCS), Buenos Aires, Argentina, 25/07/15. https://doi.org/10.1007/978-981-10-2564-8_5

Exploiting Vagueness for Multi-Agent Consensus. / Crosscombe, Michael; Lawry, Jonathan.
Multi-agent and Complex Systems: Post-proceedings of the 2nd International Workshop on Smart Simulation and Modelling for Computer Systems (SSMCS 2015). ed. / Quan Bai; Fenghui Ren; Katsuhide Fujita; Minjie Zhang; Takayuki Ito. Springer, Singapore, 2016. p. 67-78 (Studies in Computational Intelligence; Vol. 970).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Exploiting Vagueness for Multi-Agent Consensus

AU - Crosscombe, Michael

AU - Lawry, Jonathan

N1 - Conference code: 2

PY - 2016/11/16

Y1 - 2016/11/16

N2 - A framework for consensus modelling is introduced using Kleene’s three valued logic as a means to express vagueness in agents’ beliefs. Explicitly borderline cases are inherent to propositions involving vague concepts where sentences of a propositional language may be absolutely true, absolutely false or borderline. By exploiting these intermediate truth values, we can allow agents to adopt a more vague interpretation of underlying concepts in order to weaken their beliefs and reduce the levels of inconsistency, so as to achieve consensus. We consider a consensus combination operation which results in agents adopting the borderline truth value as a shared viewpoint if they are in direct conflict. Simulation experiments are presented which show that applying this operator to agents chosen at random (subject to a consistency threshold) from a population, with initially diverse opinions, results in convergence to a smaller set of more precise shared beliefs. Furthermore, if the choice of agents for combination is dependent on the payoff of their beliefs, this acting as a proxy for performance or usefulness, then the system converges to beliefs which, on average, have higher payoff.

AB - A framework for consensus modelling is introduced using Kleene’s three valued logic as a means to express vagueness in agents’ beliefs. Explicitly borderline cases are inherent to propositions involving vague concepts where sentences of a propositional language may be absolutely true, absolutely false or borderline. By exploiting these intermediate truth values, we can allow agents to adopt a more vague interpretation of underlying concepts in order to weaken their beliefs and reduce the levels of inconsistency, so as to achieve consensus. We consider a consensus combination operation which results in agents adopting the borderline truth value as a shared viewpoint if they are in direct conflict. Simulation experiments are presented which show that applying this operator to agents chosen at random (subject to a consistency threshold) from a population, with initially diverse opinions, results in convergence to a smaller set of more precise shared beliefs. Furthermore, if the choice of agents for combination is dependent on the payoff of their beliefs, this acting as a proxy for performance or usefulness, then the system converges to beliefs which, on average, have higher payoff.

KW - Agent-Based Modelling

KW - Many-Valued Logics

KW - Belief Aggregation

KW - Consensus

UR - http://www.uow.edu.au/~fren/SSMCS2015/SSMCS2015_Proceeding.pdf

UR - https://arxiv.org/abs/1607.05540

U2 - 10.1007/978-981-10-2564-8_5

DO - 10.1007/978-981-10-2564-8_5

M3 - Conference Contribution (Conference Proceeding)

SN - 9789811025631

T3 - Studies in Computational Intelligence

SP - 67

EP - 78

BT - Multi-agent and Complex Systems

A2 - Bai, Quan

A2 - Ren, Fenghui

A2 - Fujita, Katsuhide

A2 - Zhang, Minjie

A2 - Ito, Takayuki

PB - Springer, Singapore

T2 - 2nd International Workshop on Smart Simulation and Modelling Computer Systems (SSMCS)

Y2 - 25 July 2015 through 31 July 2015

ER -

Crosscombe M, Lawry J. Exploiting Vagueness for Multi-Agent Consensus. In Bai Q, Ren F, Fujita K, Zhang M, Ito T, editors, Multi-agent and Complex Systems: Post-proceedings of the 2nd International Workshop on Smart Simulation and Modelling for Computer Systems (SSMCS 2015). Springer, Singapore. 2016. p. 67-78. (Studies in Computational Intelligence). doi: 10.1007/978-981-10-2564-8_5

Exploiting Vagueness for Multi-Agent Consensus

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