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Conference papers

A Short-Term Memory for Deliberative Agents in Everyday Environments

Abstract : Humans have the impressive capability to efficiently find near-optimal solutions to complex, multi-step problems. AI planning can model such problems well, but is inefficient for realistic problems. We propose to use AI planning in combination with a short-term memory, inspired by models of human short-term memory, to structure real-world problem domains and make the planning process more efficient, while still producing satisficing solutions. We evaluate the method in the domain of a household robot.
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Contributor : Alexandra Kirsch Connect in order to contact the contributor
Submitted on : Friday, January 26, 2018 - 2:15:07 PM
Last modification on : Thursday, January 6, 2022 - 11:38:04 AM
Long-term archiving on: : Friday, May 25, 2018 - 4:57:07 AM


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  • HAL Id : hal-01693669, version 1


Ivo Chichkov, Alexandra Kirsch. A Short-Term Memory for Deliberative Agents in Everyday Environments. 2nd Global Conference on Artificial Intelligence (GCAI), 2016, Berlin, Germany. ⟨hal-01693669⟩



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