An emotion understanding framework for intelligent agents based on episodic and semantic memories

  • Authors:

  • Mohammad Kazemifard;Nasser Ghasem-Aghaee;Bryan L. Koenig;Tuncer I. Ören

  • Affiliations:

  • Department of Computer Engineering, Razi University, Kermanshah, Iran;Department of Computer Engineering, Sheikh Bahaei University and University of Isfahan, Isfahan, Iran;Computational Social Cognition, Institute of High Performance Computing, Agency for Science, Technology, and Research, Singapore, Singapore and Department of Psychology, National University of Sin ...;School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

  • Venue:
  • Autonomous Agents and Multi-Agent Systems
  • Year:
  • 2014

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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.