Multi-Agent Epistemic Explanatory Diagnosis via Reasoning about Actions
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1183-1189.
Abstract
The task of explanatory diagnosis conjectures actions to explain observations.This is a common task in real life and an essential ability of intelligent agents.It becomes more complicated in multi-agent scenarios, since agents' actions may be partially observable to other agents, and observations might involve agents' knowledge about the world or other agents' knowledge or even common knowledge of a group of agents.For example, we might want to explain the observation that p does not hold,but Ann believes p, or the observation that Ann, Bob, and Carl commonly believe p. In this paper, we formalize the multi-agent explanatory diagnosis task in the framework of dynamic epistemic logic, where Kripke models of actions are used to represent agents' partial observability of actions. Since this task is undecidable in general, we identify important decidable fragments via techniques of reducing the potentially infinite search spaces to finite ones of epistemic states or action sequences.
@inproceedings{ijcai2013p178,
title = {Multi-Agent Epistemic Explanatory Diagnosis via Reasoning about Actions},
author = {Yu, Quan and Wen, Ximing and Liu, Yongmei},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {1183--1189},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/178},
}
TY - CPAPER TI - Multi-Agent Epistemic Explanatory Diagnosis via Reasoning about Actions AU - Yu, Quan AU - Wen, Ximing AU - Liu, Yongmei T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 1183 EP - 1189 UR - https://ijcai.org/proceedings/2013/178 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: