Playing with knowledge: A virtual player for “Who Wants to Be a Millionaire?” that leverages question answering techniques
作者：
Highlights：
• We model a virtual player for “Who Wants to be a Millionaire” game.
• The virtual player uses Question Answering over Wikipedia and DBpedia knowledge.
• We performed experiments on the Italian and the English version of the game.
• The virtual player outperforms human players to correctly answer to questions of the game.
• The virtual player outperforms human players to play real games both in terms of level of the game reached and average income.
摘要
This paper describes the techniques used to build a virtual player for the popular TV game “Who Wants to Be a Millionaire?”. The player must answer a series of multiple-choice questions posed in natural language by selecting the correct answer among four different choices. The architecture of the virtual player consists of 1) a Question Answering (QA) module, which leverages Wikipedia and DBpedia datasources to retrieve the most relevant passages of text useful to identify the correct answer to a question, 2) an Answer Scoring (AS) module, which assigns a score to each candidate answer according to different criteria based on the passages of text retrieved by the Question Answering module, and 3) a Decision Making (DM) module, which chooses the strategy for playing the game according to specific rules as well as to the scores assigned to the candidate answers.
论文关键词：Language game,Question answering,Natural language processing,Artificial intelligence,Decision making
论文评审过程：Received 5 November 2013, Revised 29 January 2015, Accepted 6 February 2015, Available online 12 February 2015.
论文官网地址：https://doi.org/10.1016/j.artint.2015.02.003
