Improving sequence recognition for learning the behavior of agents

Yoav Horman, Gal A. Kaminka

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

To accomplish in their tasks, agents need to build models of other agents from observations. In open or adversarial settings, the observer agent does not know the full behavior repertoire of observed agents, and must learn a model of the other agents from its observations of their actions. This paper focuses on learning models of sequential behavior based on observed execution traces. It empirically compares sequence recognition approaches, and shows that they suffer from common deficiencies, including length-biases and inability to generalize discovered patterns. We present bias-removing and clustering methods to address these challenges, and evaluate them using synthetic and real-world data. The results show significant improvements in all the learning algorithms tested.

Original languageEnglish
Title of host publicationProceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004
EditorsN.R. Jennings, C. Sierra, L. Sonenberg, M. Tambe
Pages1332-1333
Number of pages2
StatePublished - 2004
EventProceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004 - New York, NY, United States
Duration: 19 Jul 200423 Jul 2004

Publication series

NameProceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004
Volume3

Conference

ConferenceProceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004
Country/TerritoryUnited States
CityNew York, NY
Period19/07/0423/07/04

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