Abstract
Previous research has claimed dynamic epistemic logic (DEL) to be a suitable formalism for representing essential aspects of a Theory of Mind (ToM) for
an autonomous agent. This includes the ability of the formalism to represent the reasoning involved in false-belief tasks of arbitrary order, and hence
for autonomous agents based on the formalism to become able to pass such tests. This paper provides evidence for the claims by documenting the
implementation of a DEL-based reasoning system on a humanoid robot. Our implementation allows the robot to perform cognitive perspective-taking,
in particular to reason about the first- and higherorder beliefs of other agents. We demonstrate how this allows the robot to pass a quite general class
of false-belief tasks involving human agents. Additionally, as is briefly illustrated, it allows the robot to proactively provide human agents with relevant
information in situations where a system without ToM-abilities would fail. The symbolic grounding problem of turning robotic sensor input into logical action descriptions in DEL is achieved via a perception system based on deep neural networks.
an autonomous agent. This includes the ability of the formalism to represent the reasoning involved in false-belief tasks of arbitrary order, and hence
for autonomous agents based on the formalism to become able to pass such tests. This paper provides evidence for the claims by documenting the
implementation of a DEL-based reasoning system on a humanoid robot. Our implementation allows the robot to perform cognitive perspective-taking,
in particular to reason about the first- and higherorder beliefs of other agents. We demonstrate how this allows the robot to pass a quite general class
of false-belief tasks involving human agents. Additionally, as is briefly illustrated, it allows the robot to proactively provide human agents with relevant
information in situations where a system without ToM-abilities would fail. The symbolic grounding problem of turning robotic sensor input into logical action descriptions in DEL is achieved via a perception system based on deep neural networks.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence |
| Publication date | 2020 |
| Pages | 1615-1621 |
| ISBN (Print) | 978-0-9992411-6-5 |
| DOIs | |
| Publication status | Published - 2020 |
| Event | Twenty-Ninth International Joint Conference on Artificial Intelligence - Scheduled for July 2020, postponed due to the Corona pandemic, Yokohama, Japan Duration: 7 Jan 2021 → 15 Jan 2021 https://ijcai20.org/ |
Conference
| Conference | Twenty-Ninth International Joint Conference on Artificial Intelligence |
|---|---|
| Location | Scheduled for July 2020, postponed due to the Corona pandemic |
| Country | Japan |
| City | Yokohama |
| Period | 07/01/2021 → 15/01/2021 |
| Internet address |