Learning to Automatically Solve Logic Grid Puzzles
By Arindam Mitra, Chitta Baral. 2015
35 citations recorded by the source corpus.
Logic grid puzzle is a genre of logic puzzles in which we are given (in a natural language) a scenario, the object to be deduced and certain clues. The reader has to figure out the solution using the clues provided and some generic domain constraints. In this paper, we present a system, LOGICIA, that takes a logic grid puzzle and the set of elements in the puzzle and tries to solve it by translating it to the knowledge representation and reasoning language of Answer Set Programming (ASP) and then using an ASP solver. The translation to ASP involves extraction of entities and their relations from the clues. For that we use a novel learning based approach which uses varied supervision, including the entities present in a clue and the expected representation of a clue in ASP. Our system, LOGICIA, learns to automatically translate a clue with 81.11% accuracy and is able to solve 71% of the problems of a corpus. This is the first learning system that can solve logic grid puzzles described in natural language in a fully automated manner. The code and the data will be made publicly available at http://bioai.lab . asu.edu/logicgridpuzzles.
Subfields: Artificial Intelligence, Natural Language Processing, LLM Agents & Tool Use
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Hannaneh Hajishirzi
- Ashish Sabharwal
- Wei Dang Lu
- Subhro Roy
- Peter Clark
- Lei Wang
- Dan Roth
- Yanyan Zou
In Natural Language Processing
- Hannaneh Hajishirzi
- Ashish Sabharwal
- Wei Dang Lu
- Subhro Roy
- Dan Roth
- Peter Clark
- Lei Wang
- Yanyan Zou
In LLM Agents & Tool Use
Citations in the corpus
- MAWPS: A Math Word Problem Repository (2016, background): “, 2015), logic puzzle problems (Mitra and Baral, 2015), arithmetic word problems (Hosseini et al.”
- Parsing Algebraic Word Problems into Equations (2015, background): “Recent efforts in the math domain have studied number word problems (Shi et al., 2015), logic puzzle problems (Mitra and Baral, 2015), arithmetic word problems (Hosseini et al., 2014; Roy and Roth, 2015), algebra word problems (Kushman et al., 2014; Zhou et al., 2015), and geometry word problems…”
- Data-Driven Methods for Solving Algebra Word Problems (2018, background): “Mitra and Baral (2015) solve simple word problems by categorizing their operations as part-whole, change, or comparison.”
- Text2Math: End-to-end Parsing Text into Math Expressions (2019, background): “Other types of problems have also been investigated, including probability problems (Dries et al., 2017), logic puzzle problems (Mitra and Baral, 2015; Chesani et al., 2017) and geometry problems (Seo et al., 2014, 2015).”