A Complete Epistemic Planner without the Epistemic Closed World Assumption

Authors: Hai Wan, Rui Yang, Liangda Fang, Yongmei Liu, Huada Xu

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Our experimental results show that EPK can generate solutions effectively for most of the epistemic planning problems we have considered including those without the ECWA. and Table 1 reports the comparative experimental results. We can observe that, generally speaking, our planner EPK are better than PKS in the performance except btc.
Researcher Affiliation Academia Hai Wana, Rui Yangb , Liangda Fangb, Yongmei Liub, and Huada Xua a School of Software, Sun Yat-sen University, Guangzhou, China b Dept. of Computer Science, Sun Yat-sen University, Guangzhou, China
Pseudocode Yes Algorithm 1: Plan(O, S, I, G)
Open Source Code Yes 1.http://ss.sysu.edu.cn/%7ewh/epk.html
Open Datasets No We experimentally compare EPK with PKS, and use a testbed of five domains, including two domains taken from PKS, other domains taken from contingent planning. Some domains in PKS contain functions and numerical representation, which EPK can not handle. Two domains in PKS are as follows: btc and unix series. In addition, other domains, doors, push and dispose are from classical contingent planning benchmarks. The paper mentions dataset names but does not provide specific access information like links, DOIs, or formal citations for public availability.
Dataset Splits No The paper does not explicitly specify exact dataset split percentages or absolute sample counts for training, validation, or test sets.
Hardware Specification Yes Our planner EPK is implemented in C++, and our experiments were done on a PC with Intel i7-4700MQ (2.4GHz) CPU and 4GB RAM on Linux.
Software Dependencies No The paper states that EPK is implemented in C++ but does not provide specific version numbers for any software dependencies or libraries.
Experiment Setup No The paper describes the search strategies and mentions a heuristic function, but it does not provide specific experimental setup details such as hyperparameter values or detailed training configurations.