FML-based Dynamic Assessment Agent for Human-Machine Cooperative System
  on Game of Go
  In this paper, we demonstrate the application of Fuzzy Markup Language (FML)
to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an
FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The
proposed FDAA comprises an intelligent decision-making and learning mechanism,
an intelligent game bot, a proximal development agent, and an intelligent
agent. The intelligent game bot is based on the open-source code of Facebook
Darkforest, and it features a representational state transfer application
programming interface mechanism. The proximal development agent contains a
dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a
fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket
engine and a summarization agent that is based on the estimated win rate,
real-time simulation number, and matching degree of predicted moves.
Additionally, the FML for player performance evaluation and linguistic
descriptions for game results commentary are presented. We experimentally
verify and validate the performance of the FDAA and variants of the FHMCS by
testing five games in 2016 and 60 games of Google Master Go, a new version of
the AlphaGo program, in January 2017. The experimental results demonstrate that
the proposed FDAA can work effectively for Go applications.
