Social Cognition: Memory Decay and Adaptive Information Filtering for Robust Information Maintenance

Authors

  • David Reitter Carnegie Mellon University
  • Christian Lebiere Carnegie Mellon University

DOI:

https://doi.org/10.1609/aaai.v26i1.8158

Keywords:

memory and knowledge, natural language processing, dialogue and interaction

Abstract

Two information decay methods are examined that help multi-agent systems cope with dynamic environments. The agents in this simulation have human-like memory and a mechanism to moderate their communications: they forget internally stored information via temporal decay, and they forget distributed information by filtering it as it passes through a communication network. The agents play a foraging game, in which performance depends on communicating facts and requests and on storing facts in internal memory. Parameters of the game and agent models are tuned to human data. Agent groups with moderated communication in small-world networks achieve optimal performance for typical human memory decay values, while non-adaptive agents benefit from stronger memory decay. The decay and filtering strategies interact with the properties of the network graph in ways suggestive of an evolutionary co-optimization between the human cognitive system and an external social structure.

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Published

2021-09-20

How to Cite

Reitter, D., & Lebiere, C. (2021). Social Cognition: Memory Decay and Adaptive Information Filtering for Robust Information Maintenance. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 242-248. https://doi.org/10.1609/aaai.v26i1.8158

Issue

Section

AAAI Technical Track: Cognitive Systems