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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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Dates and versions

hal-01693669 , version 1 (26-01-2018)

Identifiers

  • HAL Id : hal-01693669 , version 1

Cite

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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