Abstract
Multiagent Systems with Symbiotic Learning and
Evolution (Masbiole) has been proposed and studied,
which is a new methodology of Multiagent Systems (MAS)
based on symbiosis in the ecosystem. Masbiole employs a
method of symbiotic learning and evolution where agents
can learn or evolve according to their symbiotic
relations toward others, i.e., considering the
benefits/losses of both itself and an opponent. As a
result, Masbiole can escape from Nash Equilibria and
obtain better performances than conventional MAS where
agents consider only their own benefits. This paper
focuses on the evolutionary model of Masbiole, and its
characteristics are examined especially with an
emphasis on the behaviours of agents obtained by
symbiotic evolution. In the simulations, two ideas
suitable for the effective analysis of such behaviors
are introduced; "Match Type Tile-world (MTT)" and
"Genetic Network Programming (GNP)". MTT is a
virtual model where tile-world is improved so that
agents can behave considering their symbiotic
relations. GNP is a newly developed evolutionary
computation which has the directed graph type gene
structure and enables to analyse the decision making
mechanism of agents easily. Simulation results show
that Masbiole can obtain various kinds of behaviours
and better performances than conventional MAS in MTT by
evolution.
- (artificial
- algorithms,
- computation,
- decision
- directed
- equilibria,
- evolution,
- evolutionary
- genetic
- graph
- graph,
- intelligence),
- learning
- learning,
- making,
- match
- model,
- models,
- multi-agent
- multiagent
- nash
- network
- programming,
- symbiosis
- symbiosis,
- symbiotic
- systems,
- theory,
- tile-world
- tile-world,
- type
- virtual
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