Authors:
Chris Rozemuller
;
Mark Neerincx
and
Koen Hindriks
Affiliation:
Delft University of Technology, Netherlands
Keyword(s):
Performance, Exploration Game, Communication, Team Size, Topology, Resource Redundancy, Task Size.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bioinformatics
;
Biomedical Engineering
;
Cooperation and Coordination
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Information Systems Analysis and Specification
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Multi-Agent Systems
;
Operational Research
;
Planning and Scheduling
;
Simulation
;
Simulation and Modeling
;
Software Engineering
;
Symbolic Systems
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.
(More)