In this thesis, a novel performance evaluation framework is introduced for several
dynamic multi-agent interpretations of the Battle of the Exes scenario, which are also
proposed for the first time. The Multi-Agent BoE scenario (MBoE) was modeled as a
Markov Game and was computationally examined in 7 branches of 2-6 and 2-10 agents’
experiments (43 experiments) for two types of state-representations, two numbers of
episodes and various reward systems. New variations of Fairness (Multi-agent ...
In this thesis, a novel performance evaluation framework is introduced for several
dynamic multi-agent interpretations of the Battle of the Exes scenario, which are also
proposed for the first time. The Multi-Agent BoE scenario (MBoE) was modeled as a
Markov Game and was computationally examined in 7 branches of 2-6 and 2-10 agents’
experiments (43 experiments) for two types of state-representations, two numbers of
episodes and various reward systems. New variations of Fairness (Multi-agent Fairness
& Reward Fairness) were proposed, with the latter being used to measure the systems’
performance, as literature’s 2-agents metrics were found insufficient for n-agents
games. Furthermore, a Perfect Alternation equilibrium was introduced, defined and
evaluated, as an ideal equilibrium, robust to the number of agents and episodes of a
system. For the purposes of this thesis, it was hypothesized and eventually shown, that a
Fair and Efficient equilibrium in multi-agent dynamic environments such as MBoE does
not necessarily signify Perfect Alternation. Furthermore, new types of metrics and
indicators for measuring and evaluating the performance of a system towards Perfect
Alternation were introduced and tested: Rotation (RT, with 2 sub-metrics and 2 forms),
Alternation (ALT, 6 versions) and Proportional Individual Performance. ALT metrics
definitions were found sufficient for evaluating of Perfect Alternation, thus they was
benchmarked and tested through the series of experiments, aspiring to initiate this
branch of studies and contribute novel tools for deeper understanding of complexsystems, such as the social behavior of cognitive systems.
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