TY - GEN
T1 - Computational Theory of Mind for Human-Agent Coordination
AU - Erdogan, Emre
AU - Dignum, Frank
AU - Verbrugge, Rineke
AU - Yolum, Pınar
PY - 2022
Y1 - 2022
N2 - In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.
AB - In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.
UR - https://www.scopus.com/pages/publications/85144466517
UR - https://www.scopus.com/inward/citedby.url?scp=85144466517&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-20845-4_6
DO - 10.1007/978-3-031-20845-4_6
M3 - Conference contribution
SN - 9783031208447
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 92
EP - 108
BT - Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XV
A2 - Ajmeri, Nirav
A2 - Morris Martin, Andreasa
A2 - Savarimuthu, Bastin Tony
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Workshop on Coordination, Organizations, Institutions, and Norms for Governance of Multi-Agent Systems, COINE 2022 co-located with 21st International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2022
Y2 - 9 May 2022 through 9 May 2022
ER -