A Reasoning System for a First-Order Logic of Limited Belief
Authors: Christoph Schwering
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we present experimental results that showcase the benefit of limited belief. Our evaluation is twofold. Besides modelling toy domains to showcase the language s expressivity, we have also tested the system s performance with two popular puzzle games, Sudoku and Minesweeper. The results confirm the hypothesis that small belief levels often suffice to achieve good results. |
| Researcher Affiliation | Academia | Christoph Schwering School of Computer Science and Engineering The University of New South Wales Sydney NSW 2052, Australia c.schwering@unsw.edu.au |
| Pseudocode | No | The paper describes decision procedures and implementation details but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | LIMBO is written in C++ and available as open source.3 ... 3Source code: www.github.com/schwering/limbo |
| Open Datasets | No | The paper uses instances from 'The New York Times website' and 'Top 1465 list' for Sudoku, and '1000 randomised runs' for Minesweeper. While these sources are mentioned, they are not presented as formally accessible, public datasets with specific links, DOIs, or citations that meet the criteria for public dataset availability. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. |
| Hardware Specification | Yes | The experiments were conducted with custom implementations of both games, compiled with clang -O3, and run on an Intel Core i7-4600U CPU at 3.3 GHz. |
| Software Dependencies | No | The paper states that the code was 'compiled with clang -O3' and that LIMBO is 'written in C++', but it does not provide specific version numbers for any libraries, frameworks, or other software dependencies. |
| Experiment Setup | Yes | Both agents use iterative deepening to find their next move: first, they look for a cell (x, y) for which value(x, y) or is Mine(x, y) is known at belief level 0; if none exists, they repeat the same for belief level 1; and so on, until a specified maximum level is reached. Once a known cell is found, the corresponding information is added to the knowledge base. In the case of Minesweeper, it is sometimes necessary to guess; we then use a naive strategy that prefers cells that are not next to an uncovered field. |