30,178 research outputs found
Decentralization of Multiagent Policies by Learning What to Communicate
Effective communication is required for teams of robots to solve
sophisticated collaborative tasks. In practice it is typical for both the
encoding and semantics of communication to be manually defined by an expert;
this is true regardless of whether the behaviors themselves are bespoke,
optimization based, or learned. We present an agent architecture and training
methodology using neural networks to learn task-oriented communication
semantics based on the example of a communication-unaware expert policy. A
perimeter defense game illustrates the system's ability to handle dynamically
changing numbers of agents and its graceful degradation in performance as
communication constraints are tightened or the expert's observability
assumptions are broken.Comment: 7 page
Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality
In the modern era, each Internet user leaves enormous amounts of auxiliary
digital residuals (footprints) by using a variety of on-line services. All this
data is already collected and stored for many years. In recent works, it was
demonstrated that it's possible to apply simple machine learning methods to
analyze collected digital footprints and to create psycho-demographic profiles
of individuals. However, while these works clearly demonstrated the
applicability of machine learning methods for such an analysis, created simple
prediction models still lacks accuracy necessary to be successfully applied for
practical needs. We have assumed that using advanced deep machine learning
methods may considerably increase the accuracy of predictions. We started with
simple machine learning methods to estimate basic prediction performance and
moved further by applying advanced methods based on shallow and deep neural
networks. Then we compared prediction power of studied models and made
conclusions about its performance. Finally, we made hypotheses how prediction
accuracy can be further improved. As result of this work, we provide full
source code used in the experiments for all interested researchers and
practitioners in corresponding GitHub repository. We believe that applying deep
machine learning for psycho-demographic profiling may have an enormous impact
on the society (for good or worse) and provides means for Artificial
Intelligence (AI) systems to better understand humans by creating their
psychological profiles. Thus AI agents may achieve the human-like ability to
participate in conversation (communication) flow by anticipating human
opponents' reactions, expectations, and behavior
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