A General Multi-agent Epistemic Planner Based on Higher-order Belief Change

Authors: Xiao Huang, Biqing Fang, Hai Wan, Yongmei Liu

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 {huangx233,fangbq3}@mail2.sysu.edu.cn, {wanhai,ymliu}@mail.sysu.edu.cn
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].