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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Online Agent Supervision in the Situation Calculus
Authors: Bita Banihashemi, Giuseppe De Giacomo, Yves Lespérance
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main results of this paper are (i) a formalization of the online maximally permissive supervisor, (ii) a sound and complete technique for execution of the agent as constrained by such a supervisor, and (iii) a new type of lookahead search construct that ensures nonblockingness over such online executions. |
| Researcher Affiliation | Academia | Bita Banihashemi York University Toronto, Canada EMAIL Giuseppe De Giacomo Sapienza Universit a di Roma Roma, Italy EMAIL Yves Lesp erance York University Toronto, Canada EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It defines logical predicates and language constructs but not procedural pseudocode. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not discuss datasets for training or their public availability. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper refers to theoretical frameworks (Situation Calculus, Con Golog) but does not provide specific ancillary software details with version numbers (e.g., libraries, solvers). |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameters or training configurations. |