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..
A General Multi-agent Epistemic Planner Based on Higher-order Belief Change
Authors: Xiao Huang, Biqing Fang, Hai Wan, Yongmei Liu
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show the viability of our approach. Our experiments were run on a Linux machine with 2.50GHz CPU and 4GB RAM. Our experimental results are shown in Table 1. |
| Researcher Affiliation | Academia | Xiao Huang, Biqing Fang, Hai Wan, Yongmei Liu Dept. of Computer Science, Sun Yat-sen University, Guangzhou 510006, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms in text, for example in Section 4 'Our Algorithms', but does not present a formal pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | Yes | 1The link to our planner and domain sources is: https://github.com/sysulic/MEPK. |
| Open Datasets | No | We evaluate MEPK with Selective-communication (SC) and Collaboration-and-communication (CC) domains adapted from [Kominis and Geffner, 2015], and Grapevine from [Muise et al., 2015], where SC is called Corridor. We also made up three domains: Assembly-line (AL), and domains adapted from the classic Gossip problem [Attamah et al., 2014] and the knowledge game Hexa [van Ditmarsch, 2001]. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits, percentages, or sample counts. |
| Hardware Specification | Yes | Our experiments were run on a Linux machine with 2.50GHz CPU and 4GB RAM. |
| Software Dependencies | No | Based on the theoretic work, we have developed EPDDL an extension of PDDL [Mc Dermott et al., 1998], to describe multi-agent epistemic planning problems, and with naive implementations of Satoh s revision and Winslett s update operators, implemented a multi-agent epistemic planner MEPK. |
| Experiment Setup | No | In this section, we present our modeling framework for multi-agent epistemic planning (MEP), which is adapted from that for single-agent epistemic planning in [Wan et al., 2015]. |