Qualitative Reasoning with Modelica Models
Authors: Matthew Klenk, Johan de Kleer, Daniel Bobrow, Bill Janssen
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support this contribution with examples and a case study. ... We demonstrate this contribution through detailed examples, a case study that shows the additional constraints result in an exponential reduction in the size of the envisionment. ... To illustrate the value of these additional constraints, we conducted the following case study. ... In each of these systems, we observed a significant reduction in the number of qualitative states. |
| Researcher Affiliation | Industry | Matthew Klenk, Johan de Kleer, Daniel G. Bobrow, Bill Janssen Palo Alto Research Center 3333 Coyote Hill Rd Palo Alto, CA, 94303 klenk,dekleer,bobrow,janssen@parc.com |
| Pseudocode | Yes | Algorithm 1: Compute the set of unchanged variables, UV , from the set of previous constraints, PC, active constraints, AC, and variables V . CC is the queue of constraints through which discrete changes may propagate. NDV is the set variables that are guaranteed to be maintain their values through events. ... begin UV V CC (PC AC) (PC AC) while not Empty(CC) do c dequeue(CC) foreach v variables(c) do if v NDV ) then remove v from UV foreach c constraints(v) do enqueue(c,CC) return UV |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it state that the code is available or will be released. |
| Open Datasets | No | The paper describes using 'simple systems' built from 'electrical and mechanical components from the Modelica Standard Library' for a case study, but does not provide access information (link, DOI, repository, or citation with author/year) for any specific dataset used in a publicly available or open manner. |
| Dataset Splits | No | The paper discusses a 'case study' involving different Modelica models (Brake, RC-ladder, Adby), but it does not specify any dataset splits (e.g., train/validation/test percentages or sample counts) as would be typical for machine learning or data-driven experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'Modelica,' 'Open Modelica,' 'DASSL,' and 'Macsyma,' but it does not provide specific version numbers for these or any other ancillary software components, which is required for reproducibility. |
| Experiment Setup | No | The paper describes the 'simple systems' used in the case study (Brake, RC-ladder, Adby) and their purpose, but it does not provide specific experimental setup details such as concrete hyperparameter values or system-level training configurations (e.g., learning rates, batch sizes, optimizer settings) that are typically found in experimental papers. |