291 research outputs found
Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition
This paper assumes the hypothesis that human learning is perception based,
and consequently, the learning process and perceptions should not be
represented and investigated independently or modeled in different simulation
spaces. In order to keep the analogy between the artificial and human learning,
the former is assumed here as being based on the artificial perception. Hence,
instead of choosing to apply or develop a Computational Theory of (human)
Perceptions, we choose to mirror the human perceptions in a numeric
(computational) space as artificial perceptions and to analyze the
interdependence between artificial learning and artificial perception in the
same numeric space, using one of the simplest tools of Artificial Intelligence
and Soft Computing, namely the perceptrons. As practical applications, we
choose to work around two examples: Optical Character Recognition and Iris
Recognition. In both cases a simple Turing test shows that artificial
perceptions of the difference between two characters and between two irides are
fuzzy, whereas the corresponding human perceptions are, in fact, crisp.Comment: 5th Int. Conf. on Soft Computing and Applications (Szeged, HU), 22-24
Aug 201
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Value Decomposition (VD) aims to deduce the contributions of agents for
decentralized policies in the presence of only global rewards, and has recently
emerged as a powerful credit assignment paradigm for tackling cooperative
Multi-Agent Reinforcement Learning (MARL) problems. One of the main challenges
in VD is to promote diverse behaviors among agents, while existing methods
directly encourage the diversity of learned agent networks with various
strategies. However, we argue that these dedicated designs for agent networks
are still limited by the indistinguishable VD network, leading to homogeneous
agent behaviors and thus downgrading the cooperation capability. In this paper,
we propose a novel Contrastive Identity-Aware learning (CIA) method, explicitly
boosting the credit-level distinguishability of the VD network to break the
bottleneck of multi-agent diversity. Specifically, our approach leverages
contrastive learning to maximize the mutual information between the temporal
credits and identity representations of different agents, encouraging the full
expressiveness of credit assignment and further the emergence of
individualities. The algorithm implementation of the proposed CIA module is
simple yet effective that can be readily incorporated into various VD
architectures. Experiments on the SMAC benchmarks and across different VD
backbones demonstrate that the proposed method yields results superior to the
state-of-the-art counterparts. Our code is available at
https://github.com/liushunyu/CIA
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