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Title: An emotion understanding framework for intelligent agents based on episodic and semantic memories
Authors: Kazemifard, M.
Ghasem-Aghaee, N.
Koenig, B.L. 
Ören, T.I.
Keywords: Artificial intelligence
Emotion understanding ability
Episodic memory
Intelligent agents
Multi-agent simulation
Semantic memory
Issue Date: Jan-2014
Citation: Kazemifard, M., Ghasem-Aghaee, N., Koenig, B.L., Ören, T.I. (2014-01). An emotion understanding framework for intelligent agents based on episodic and semantic memories. Autonomous Agents and Multi-Agent Systems 28 (1) : 126-154. ScholarBank@NUS Repository.
Abstract: Emotional intelligence is the ability to process information about one's own emotions and the emotions of others. It involves perceiving emotions, understanding emotions, managing emotions and using emotions in thought processes and in other activities. Emotion understanding is the cognitive activity of using emotions to infer why an agent is in an emotional state and which actions are associated with the emotional state. For humans, knowledge about emotions includes, in part, emotional experiences (episodic memory) and abstract knowledge about emotions (semantic memory). In accordance with the need for more sophisticated agents, the current research aims to increase the emotional intelligence of software agents by introducing and evaluating an emotion understanding framework for intelligent agents. The framework organizes the knowledge about emotions using episodic memory and semantic memory. Its episodic memory learns by storing specific details of emotional events experienced firsthand or observed. Its semantic memory is a lookup table of emotion-related facts combined with semantic graphs that learn through abstraction of additional relationships among emotions and actions from episodic memory. The framework is simulated in a multi-agent system in which agents attempt to elicit target emotions in other agents. They learn what events elicit emotions in other agents through interaction and observation. To evaluate the importance of different memory components, we run simulations with components "lesioned". We show that our framework outperformed Q-learning, a standard method for machine learning. © 2012 The Author(s).
Source Title: Autonomous Agents and Multi-Agent Systems
ISSN: 13872532
DOI: 10.1007/s10458-012-9214-9
Appears in Collections:Staff Publications

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