Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
A Coupled Operational Semantics for Goals and Commitments
Authors: Pankaj R. Telang, Munindar P. Singh, Neil Yorke-Smith
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This article provides a combined operational semantics for goals and commitments by relating their respective life cycles as a basis for how these concepts (1) cohere for an individual agent and (2) engender cooperation among agents. Our semantics yields important desirable properties of convergence of the configurations of cooperating agents, thereby delineating some theoretically well-founded yet practical modes of cooperation in a multiagent system. |
| Researcher Affiliation | Collaboration | Pankaj R. Telang EMAIL SAS Institute Inc. 100 SAS Campus Dr., Cary, NC 27513, USA Munindar P. Singh EMAIL North Carolina State University Raleigh, NC 27695, USA Neil Yorke-Smith EMAIL Delft University of Technology & American University of Beirut 2600GA Delft, The Netherlands |
| Pseudocode | Yes | Algorithm 1 Expansion of example practical rule template (Section 3.4.1) 1: for G Gx do 2: if G is Active then 3: T CSG(G) 4: for C T do 5: Apply action of rule to C |
| Open Source Code | No | The paper mentions that "Baldoni and colleagues (Baldoni et al., 2015) implement our earlier set of practical rules in Ja Ca Mo+", referring to other researchers implementing their prior work. However, there is no explicit statement or link provided by the authors of this paper indicating that the code for the described methodology is open-source or publicly available. |
| Open Datasets | No | This paper focuses on providing a formal operational semantics and theoretical proofs for goals and commitments. It does not describe any experiments that utilize or rely on specific datasets, nor does it provide access information for any datasets. While it mentions an "application on a well-known case study", no dataset details are given for this. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments with datasets. Therefore, there is no information provided regarding training/test/validation dataset splits. |
| Hardware Specification | No | This paper presents a theoretical framework and formal proofs, without describing any computational experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical, focusing on formal semantics and proofs. It does not describe any implementation details that would require specific software dependencies with version numbers. While it references concepts like "BDI architecture" and other agent programming languages in related work, it does not list software dependencies for its own methodology. |
| Experiment Setup | No | This paper is theoretical, providing a formal operational semantics and proofs, rather than conducting empirical experiments. Consequently, there are no details provided regarding experimental setup, hyperparameters, or system-level training settings. |