Reasoning about Space and Change with Answer Set Programming Modulo Theories
Authors: Przemysław Andrzej Wałęga
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The work accomplished so far consists of theoretical investigation of a framework based on a paradigm of Answer Set Programming Modulo Theories and its implementation. The developed system enables to integrate geometrical and qualitative spatial information, reason about indirect spatial effects and perform non-monotonic reasoning in a context of spatio-temporal contexts.Our implementation of ASPMT(QS) is built on top of ASPMT2SMT [Bartholomew and Lee, 2014] a compiler that translates a tight fragment of ASPMT into SMT instances. |
| Researcher Affiliation | Academia | Przemysław Andrzej Wał ega University of Warsaw, Institute of Philosophy, Poland |
| Pseudocode | No | No pseudocode or algorithm blocks found. |
| Open Source Code | Yes | A prototypical implementation of the system is available online publicly from Docker Hub, a cloud-based registry service for building and shipping applications: https://hub.docker.com/r/ spatialreasoning/aspmtqs/. It contains the core system, minimal working examples, short description and installation instructions. |
| Open Datasets | No | The paper describes a framework and its application to examples rather than empirical evaluation on a specific publicly available dataset. No concrete access information for a dataset for training is provided. |
| Dataset Splits | No | The paper does not describe a machine learning experiment with explicit dataset splits for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are mentioned for running experiments. |
| Software Dependencies | No | The paper mentions 'ASPMT2SMT' and 'Z3 an off the shelf SMT solver' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | No | The paper describes the framework and its application but does not provide specific experimental setup details such as hyperparameters or training configurations. |