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Project

Solving Combinatorial and Probabilistic Problems in Natural Language.

This project wants to develop a fully automated approach to solving exercises about combinatorics and probability that can be found in introductory textbooks on discrete mathematics. The ability to solve such problems is an important cognitive and intellectual skill as it is evaluated as part of academic admission tests such as SAT, GMAT and GRE. The combinatorics and probability questions will be formulated in natural language and the task will be to automatically answer these questions. We shall develop a two-step approach for tackling this task. In the first step, a question formulated in natural language will be analysed and transformed into a high-level model specified in a declarative language. In the second step, the high-level model will be solved solved using the inference mechanisms of for the declarative modeling language. The language and its solvers will be based on principles of probabilistic programming, is an increasingly popular programming paradigm. While the immediate goal is to solve textbook exercises, the long term goal is to contribute to the automation of probabilistic and combinatorics problem solving and to enable the modeling and programming for such problems in natural language, two goals that are highly relevant to cognitive computing and artificial intelligence
Date:1 Jan 2018 →  31 Dec 2021
Keywords:NATURAL LANGUAGE UNDERSTANDING, ARTIFICIAL INTELLIGENCE
Disciplines:Applied mathematics in specific fields
Project type:Collaboration project