158 research outputs found
From fuzzy to annotated semantic web languages
The aim of this chapter is to present a detailed, selfcontained and comprehensive account of the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions
A Generic Multi-Player Transformation Algorithm for Solving Large-Scale Zero-Sum Extensive-Form Adversarial Team Games
Many recent practical and theoretical breakthroughs focus on adversarial team
multi-player games (ATMGs) in ex ante correlation scenarios. In this setting,
team members are allowed to coordinate their strategies only before the game
starts. Although there existing algorithms for solving extensive-form ATMGs,
the size of the game tree generated by the previous algorithms grows
exponentially with the number of players. Therefore, how to deal with
large-scale zero-sum extensive-form ATMGs problems close to the real world is
still a significant challenge. In this paper, we propose a generic multi-player
transformation algorithm, which can transform any multi-player game tree
satisfying the definition of AMTGs into a 2-player game tree, such that finding
a team-maxmin equilibrium with correlation (TMECor) in large-scale ATMGs can be
transformed into solving NE in 2-player games. To achieve this goal, we first
introduce a new structure named private information pre-branch, which consists
of a temporary chance node and coordinator nodes and aims to make decisions for
all potential private information on behalf of the team members. We also show
theoretically that NE in the transformed 2-player game is equivalent TMECor in
the original multi-player game. This work significantly reduces the growth of
action space and nodes from exponential to constant level. This enables our
work to outperform all the previous state-of-the-art algorithms in finding a
TMECor, with 182.89, 168.47, 694.44, and 233.98 significant improvements in the
different Kuhn Poker and Leduc Poker cases (21K3, 21K4, 21K6 and 21L33). In
addition, this work first practically solves the ATMGs in a 5-player case which
cannot be conducted by existing algorithms.Comment: 9 pages, 5 figures, NIPS 202
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