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

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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.

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Notes

  1. ‘Bs’ means multiple instances of B

  2. The concept of “standard” refers to value that agents use to evaluate a manner of behaving [61].

  3. To fix the number of interactions for all the simulations, we set the number of agents and number of simulation cycles per run such that their multiplication product is equal. For example, the number of interactions of a run with 10 agents and 80 cycles was equal to another run with 20 agents and 40 cycles.

  4. This model was restricted in that it did not incorporate embodied, automatic, and associative accounts of emotion generation. This clearly is a limitation when modeling emotional intelligence.

  5. Since goal hierarchies in many applications, such as [62], use a small number of goals with large number of sub-goals and to make our implementation easier, we used only 2, 3, or 4 goals for each agent.

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Acknowledgments

We appreciate the valuable comments of Andrew Ortony and Martin Saerbeck, Institute of High Performance Computing, Agency for Science Technology and Research (A*STAR), Singapore, during the preparation of this paper. The first author appreciates the support of S. Daeichin during this study.

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Authors and Affiliations

  1. Department of Computer Engineering, Razi University, Kermanshah, Iran

    Mohammad Kazemifard

  2. Department of Computer Engineering, Sheikh Bahaei University and University of Isfahan, Isfahan, Iran

    Nasser Ghasem-Aghaee

  3. Computational Social Cognition, Institute of High Performance Computing, Agency for Science, Technology, and Research, Singapore, Singapore

    Bryan L. Koenig

  4. Department of Psychology, National University of Singapore, Singapore, Singapore

    Bryan L. Koenig

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer I. Ören

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  1. Mohammad Kazemifard
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  2. Nasser Ghasem-Aghaee
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  4. Tuncer I. Ören
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Correspondence to Mohammad Kazemifard.

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This work is based on the Ph.D. dissertation of the first author developed at the Department of Computer Engineering, University of Isfahan, Isfahan, Iran.

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Kazemifard, M., Ghasem-Aghaee, N., Koenig, B.L. et al. An emotion understanding framework for intelligent agents based on episodic and semantic memories. Auton Agent Multi-Agent Syst 28, 126–153 (2014). https://doi.org/10.1007/s10458-012-9214-9

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