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].
On the Power and Limitations of Deception in Multi-Robot Adversarial Patrolling
Authors: Noga Talmor, Noa Agmon
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have fully implemented the deception mechanisms, and following an empirical evaluation, report the tradeoff between deception and probability of penetration detection along the perimeter in several cases. |
| Researcher Affiliation | Academia | Noga Talmor and Noa Agmon Department of Computer Science, Bar-Ilan University, Israel EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Seemingly Random Patrol |
| Open Source Code | No | The paper states 'We have fully implemented the deception mechanisms' but does not provide a link or explicit statement about the code being open-source or publicly available. |
| Open Datasets | No | The paper describes a theoretical perimeter setup ('P into N identical time segments') and does not refer to a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not mention specific dataset split information (percentages, counts, or standard splits) as it does not use a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, libraries, or solvers). |
| Experiment Setup | No | The paper describes the algorithmic logic and models but does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or training schedules. |