Synthesis of Deceptive Strategies in Reachability Games with Action Misperception
Authors: Abhishek N. Kulkarni, Jie Fu
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our algorithm using a robot motion planning in an adversarial environment. and When Alg. 2 is applied to the above hypergame graph, 2106 out of 4096 states are identiļ¬ed as DASW states. The projection of DASW states onto game state space results in 1172 states, while the ASW region has the size of 934 states. This means that 1172 934 = 238 game states that were not almost-sure winning for P1 became winning for her, when P1 uses the DASW strategy. |
| Researcher Affiliation | Academia | Abhishek N. Kulkarni and Jie Fu Worcester Polytechnic Institute, Worcester, USA {ankulkarni, jfu2}@wpi.edu |
| Pseudocode | Yes | Algorithm 1 Almost-Sure Winning Region [Mazala, 2002] and Algorithm 2 Deceptive Almost-Sure Winning Region for P1 |
| Open Source Code | No | The paper does not contain any statement about making the source code for their methodology publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes a 'robot motion planning example over a 4x4 gridworld' and states 'A game on graph representing above scenario can be constructed using the product operation given in [Baier and Katoen, 2008, Def. 4.16].' This describes a constructed scenario, not a publicly available dataset with concrete access information (link, DOI, specific citation with author/year for the dataset itself). |
| Dataset Splits | No | The paper describes a theoretical framework and its application to an illustrative example, but it does not involve training models on datasets, and therefore does not specify training, validation, or test data splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or cloud resources) used for running its illustrative example or any computations. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library or solver names with version numbers, needed to replicate the described algorithms or illustrative example. |
| Experiment Setup | No | The paper describes the setup of its 'robot motion planning example' (e.g., 4x4 gridworld, action sets, initial misperception) as an illustrative scenario for applying their algorithm, but it does not provide 'experimental setup details' such as hyperparameters, training configurations, or system-level settings relevant to empirical evaluation. |