Title
On the effects of team size and communication load on the performance in exploration games
Author
Rozemuller, C.
Neerincx, M.A.
Hindriks, K.V.
Contributor
van den Herik, J. (editor)
Rocha, A.P. (editor)
Publication year
2018
Abstract
Exploration games are games where agents (or robots) need to search resources and retrieve these resources. In principle, performance in such games can be improved either by adding more agents or by exchanging more messages. However, both measures are not free of cost and it is important to be able to assess the trade-off between these costs and the potential performance gain. The focus of this paper is on improving our understanding of the performance gain that can be achieved either by adding more agents or by increasing the communication load. Performance gain moreover is studied by taking several other important factors into account such as environment topology and size, resource-redundancy, and task size. Our results suggest that there does not exist a decision function that dominates all other decision functions, i.e. is optimal for all conditions. Instead we find that (i) for different team sizes and communication strategies different agent decision functions perform optimal, and that (ii) optimality of decision functions also depends on environment and task parameters. We also find that it pays off to optimize for environment topologies. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. Institute for Systems and Technologies of Information, Control and Communication (INSTICC)
Subject
Exploration Game
Performance
Resource Redundancy
Task Size
Team Size
Topology
Artificial intelligence
Communication
Economic and social effects
Redundancy
Communication load
Communication strategy
Decision functions
Optimality
Performance Gain
To reference this document use:
http://resolver.tudelft.nl/uuid:b9172188-e0f3-468a-8fed-378898ed8062
TNO identifier
788783
Publisher
SciTePress
ISBN
9789897582752
Source
10th International Conference on Agents and Artificial Intelligence, ICAART 2018. 16 January 2018 through 18 January 2018, 221-230
Document type
conference paper