In recent years, artificial intelligence (AI) tools, including natural language processing (NLP) techniques, have become increasingly sophisticated, achieving exceptional results in a variety of tasks. NLP techniques are specifically designed to understand human language and produce suitable responses, thus enabling communication between humans and artificial agents. Other studies also introduced goal-oriented agents that can autonomously navigate virtual or videogame environments. So far, NLP techniques and goal-oriented agents have typically been developed individually, rather than being combined into unified methods. Researchers at Georgia Institute of Technology and Facebook AI Research have recently explored the possibility of equipping goal-driven agents with NLP capabilities so that they can speak with other characters and complete desirable actions within fantasy game environments.

This paper presents a semantically grounded method for extracting commonsense knowledge. First, commonsense rules are identified, e.g., one cannot see imaginary objects. Second, those rules are combined with a basic semantic representation in order to infer commonsense knowledge facts, e.g. one cannot see a flying carpet. Further combinations of semantic relations with inferred commonsense facts are proposed and analyzed. Results show that this novel method is able to extract thousands of commonsense facts with little human interaction and high accuracy.

Collecting commonsense from text with the aid of a game can reduce the cost and effort of creating large knowledge bases. In this paper, we design, implement, and evaluate an online game that classifies, with input from players, text extracted from the Web as commonsense knowledge, domain-specific knowledge or nonsense. We also create a knowledge base that includes commonsense facts in natural language and information on how common a given fact is. The game is currently available for play on the Web and on Facebook, and under constant improvement. The creation of a continuous scale to classify commonsense helped during evaluation of the data by clearly identifying which knowledge is reliable and which needs further qualification. When comparing our results to other similar knowledge acquisition systems, our Turing Game performs better with respect to coverage,redundancy, and reliability of the commonsense acquired.