Destabilising Conventions Using Temporary Interventions

Marchant, James; Griffiths, Nathan; Leeke, Matthew; Franks, Henry

doi:10.1007/978-3-319-25420-3_10

Cited by 5 publications

(12 citation statements)

References 16 publications

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“…Agents use a simplified Q-Learning algorithm to learn the most beneficial choice. We utilise the 10-action game as used by [14] to avoid the issues of small convention spaces raised in Section 2 and to allow comparison to previous work. They have a chance to randomly choose their action (p explore = 0.25) or else choose the most beneficial one.…”

Section: Methodsmentioning

confidence: 99%

“…This paper expands the state of the art by considering the effect of restricted observations on the ability to robustly and efficiently place FS agents in static, real-world topologies. Convention destabilisation [14] and dynamic topologies [13] will be investigated in future work.…”

Section: Related Workmentioning

confidence: 99%

“…We assume that the set of nodes, V , is known but the set of edges, E, (and hence neighbours and degree of a node) is not. Finding the highest degree nodes is desirable since fixed strategy agent placement by degree consistently produces effective convention emergence [8,9,13,14] but without requiring computationally expensive metrics such as betweenness centrality. The degree of nodes can be entirely derived from local information and, as such, is an applicable heuristic within partially observable networks.…”

Section: Partial Observability Algorithmmentioning

confidence: 99%

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Convention Emergence in Partially Observable Topologies

Marchant

Griffiths

2017

Autonomous Agents and Multiagent Systems

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Abstract. In multi-agent systems it is often desirable for agents to adhere to standards of behaviour that minimise clashes and wasting of (limited) resources. In situations where it is not possible or desirable to dictate these standards globally or via centralised control, convention emergence offers a lightweight and rapid alternative. Placing fixed strategy agents within a population, whose interactions are constrained by an underlying network, has been shown to facilitate faster convention emergence with some degree of control. Placing these fixed strategy agents at topologically influential locations (such as high-degree nodes) increases their effectiveness. However, finding such influential locations often assumes that the whole network is visible or that it is feasible to inspect the whole network in a computationally practical time, a fact not guaranteed in many real-world scenarios. We present an algorithm, PO-Place, that finds influential nodes given a finite number of network observations. We show that PO-Place finds sets of nodes with similar reach and influence to the set of high-degree nodes and we then compare the performance of PO-Place to degree placement for convention emergence in several real-world topologies.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Partial Observability Algorithmmentioning

confidence: 99%

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Convention Emergence in Partially Observable Topologies

Marchant

Griffiths

2017

Autonomous Agents and Multiagent Systems

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“…We have previously [13,15] investigated using fixed strategy agents in static topologies to cause members of the dominant convention to change their adopted convention and hence destabilise it. We found that this required substantially more fixed strategy agents than is needed to influence conventions before emergence.…”

Section: Related Workmentioning

confidence: 99%

“…Small numbers of these agents are able to influence much larger populations [21], especially when placed using appropriate heuristics [7,10]. Fixed strategy agents can also be used to cause a system to abandon an already established convention in favour of an alternative [13,15].…”

Section: Introductionmentioning

confidence: 99%

Manipulating Conventions in a Particle-Based Topology

Marchant

Griffiths

2016

Lecture Notes in Computer Science

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Abstract. Coordination is essential to the effective operation of multiagent systems. Convention emergence offers a low-cost and decentralised method of ensuring compatible actions and behaviour, without requiring the imposition of global rules. This is of particular importance in environments with no centralised control or where agents belong to different, possibly conflicting, parties. The timely emergence of robust conventions can be facilitated and manipulated via the use of fixed strategy agents, who attempt to influence others into adopting a particular strategy. Although fixed strategy agents have previously been investigated, they have not been considered in dynamic networks. In this paper, we explore the emergence of conventions within a dynamic network, and examine the effectiveness of fixed strategy agents in this context. Using established placement heuristics we show how such agents can encourage convention emergence, and we examine the impact of the dynamic nature of the network. We introduce a new heuristic, Life-Degree, to enable this investigation. Finally, we consider the ability of fixed strategy agents to manipulate already established conventions, and investigate the effectiveness of placement heuristics in this domain.

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