Parallel Behavior Composition for Manufacturing
Authors: Paolo Felli, Brian Logan, Sebastian Sardina
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we extend classical AI behavior composition to manufacturing settings. We first introduce a novel solution concept for manufacturing composition, target production processes, that are able to manufacture multiple instances of a product simultaneously in a given production plant. We then propose a technique for synthesizing the largest target production process, together with an associated controller for the machines in the plant. (from Abstract) and "We have defined the problem, and provided a notion of adequacy for solutions in the form of TPPs that respect requirements R1-R5." (from Conclusions) and "Proof. (Sketch) Let H ,P be the set of histories of S that may be obtained by running the controller P in S to match all the actions in a given trace of an m-TTP T (see [De Giacomo and Sardina, 2007; De Giacomo et al., 2013])." (from Theorem 1 proof sketch). The paper is primarily about formal definitions, properties, and a proof sketch for a theoretical framework. There are no empirical studies, datasets, or performance evaluations. |
| Researcher Affiliation | Academia | Paolo Felli University of Nottingham, UK paolo.felli@nottingham.ac.uk Brian Logan University of Nottingham, UK bsl@cs.nott.ac.uk Sebastian Sardina RMIT University, Australia sebastian.sardina@rmit.edu.au |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it include an explicit code release statement or mention code in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using datasets, thus no information regarding public or open datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with datasets, thus no information about dataset splits for training, validation, or testing is provided. |
| Hardware Specification | No | The paper focuses on theoretical formalizations and does not describe any computational experiments or their execution environment, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation or experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical contributions and does not include empirical experiments, therefore no specific experimental setup details such as hyperparameters or training configurations are provided. |