Towards an Approach for Modelling Uncertain Theory of Mind in Multi-Agent Systems

Stefan Sarkadi; Alison R. Panisson; Rafael H. Bordini; Peter John McBurney; Simon Dominic Parsons

Towards an Approach for Modelling Uncertain Theory of Mind in Multi-Agent Systems

Stefan Sarkadi, Alison R. Panisson, Rafael H. Bordini, Peter John McBurney, Simon Dominic Parsons

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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Abstract

Applying Theory of Mind to multi-agent systems enables agents to model and reason about other agents' minds. Recent work shows that this ability could increase the performance of agents, making them more efficient than agents that lack this ability.
However, modelling others agents' minds is a difficult task, given that it involves many factors of uncertainty, e.g., the uncertainty of the communication channel, the uncertainty of reading other agents correctly, and the uncertainty of trust in other agents.
In this paper, we explore how agents acquire and update Theory of Mind under conditions of uncertainty.
To represent uncertain Theory of Mind, we add probability estimation on a formal semantics model for agent communication based on the BDI architecture and agent communication languages.

Original language	English
Title of host publication	Proceedings of the 6th International Conference on Agreement Technologies
Subtitle of host publication	co-located with EUMAS 2018
Number of pages	15
Publication status	Published - 6 Dec 2018

Keywords

Theory of Mind
Uncertainty
Multi-Agent Systems
Socially Aware AI

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AT_UforToMAccepted author manuscript, 316 KB

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title = "Towards an Approach for Modelling Uncertain Theory of Mind in Multi-Agent Systems",

abstract = "Applying Theory of Mind to multi-agent systems enables agents to model and reason about other agents' minds. Recent work shows that this ability could increase the performance of agents, making them more efficient than agents that lack this ability. However, modelling others agents' minds is a difficult task, given that it involves many factors of uncertainty, e.g., the uncertainty of the communication channel, the uncertainty of reading other agents correctly, and the uncertainty of trust in other agents. In this paper, we explore how agents acquire and update Theory of Mind under conditions of uncertainty. To represent uncertain Theory of Mind, we add probability estimation on a formal semantics model for agent communication based on the BDI architecture and agent communication languages.",

keywords = "Theory of Mind, Uncertainty, Multi-Agent Systems, Socially Aware AI",

author = "Stefan Sarkadi and Panisson, {Alison R.} and Bordini, {Rafael H.} and McBurney, {Peter John} and Parsons, {Simon Dominic}",

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T1 - Towards an Approach for Modelling Uncertain Theory of Mind in Multi-Agent Systems

AU - Sarkadi, Stefan

AU - Panisson, Alison R.

AU - Bordini, Rafael H.

AU - McBurney, Peter John

AU - Parsons, Simon Dominic

PY - 2018/12/6

Y1 - 2018/12/6

N2 - Applying Theory of Mind to multi-agent systems enables agents to model and reason about other agents' minds. Recent work shows that this ability could increase the performance of agents, making them more efficient than agents that lack this ability. However, modelling others agents' minds is a difficult task, given that it involves many factors of uncertainty, e.g., the uncertainty of the communication channel, the uncertainty of reading other agents correctly, and the uncertainty of trust in other agents. In this paper, we explore how agents acquire and update Theory of Mind under conditions of uncertainty. To represent uncertain Theory of Mind, we add probability estimation on a formal semantics model for agent communication based on the BDI architecture and agent communication languages.

AB - Applying Theory of Mind to multi-agent systems enables agents to model and reason about other agents' minds. Recent work shows that this ability could increase the performance of agents, making them more efficient than agents that lack this ability. However, modelling others agents' minds is a difficult task, given that it involves many factors of uncertainty, e.g., the uncertainty of the communication channel, the uncertainty of reading other agents correctly, and the uncertainty of trust in other agents. In this paper, we explore how agents acquire and update Theory of Mind under conditions of uncertainty. To represent uncertain Theory of Mind, we add probability estimation on a formal semantics model for agent communication based on the BDI architecture and agent communication languages.

KW - Theory of Mind

KW - Uncertainty

KW - Multi-Agent Systems

KW - Socially Aware AI

UR - https://link.springer.com/chapter/10.1007/978-3-030-17294-7_1

M3 - Conference paper

BT - Proceedings of the 6th International Conference on Agreement Technologies

ER -

Towards an Approach for Modelling Uncertain Theory of Mind in Multi-Agent Systems

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Keywords

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