EEIA: The Extended Efficiency Improvement Advisor
Date
2018-07-25
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Abstract
In the past, the Efficiency Improvement Advisor (EIA) has been successfully applied to several dynamic problems. By learning recurring tasks, it was able to correct inefficient behavior in multiagent systems. We present an extension to the advisor which allows certain known-ahead knowledge to be exploited. This extension unobtrusively guides autonomous agents to follow a plan, while retaining the dynamic abilities of those agents. Unlike other similar approaches which introduce planning functionality, this does not require always-on communications. The extended advisor’s planning abilities work in tandem with the original learning abilities to create additional efficiency gains. The abilities of the extended advisor (including the introduction of planning, the preservation of dynamism, and mixing certain knowledge with learned knowledge) are evaluated in 2 different problem domains. First, the advisor is applied to the familiar arcade game: Whack-a-mole. Then, Pickup and Delivery is considered, which is similar to coordinating a taxi service.
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Multi-agent Systems, Pickup and Delivery, Whack-a-mole
Citation
Nygren, N. (2018). EEIA: The Extended Efficiency Improvement Advisor (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/32705