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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |