A Short-Term Memory for Deliberative Agents in Everyday Environments

13 pages•Published: September 29, 2016

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.

Keyphrases: ai planning, domestic robots, short term memory model

In: Christoph Benzmüller, Geoff Sutcliffe and Raul Rojas (editors). GCAI 2016. 2nd Global Conference on Artificial Intelligence, vol 41, pages 187-199.

Links:	https://easychair.org/publications/paper/xJQ
	https://doi.org/10.29007/r5x5

BibTeX entry

@inproceedings{GCAI2016:Short_Term_Memory_Deliberative,
  author    = {Ivo Chichkov and Alexandra Kirsch},
  title     = {A Short-Term Memory for Deliberative Agents in Everyday Environments},
  booktitle = {GCAI 2016. 2nd Global Conference on Artificial Intelligence},
  editor    = {Christoph Benzmüller and Geoff Sutcliffe and Raul Rojas},
  series    = {EPiC Series in Computing},
  volume    = {41},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/xJQ},
  doi       = {10.29007/r5x5},
  pages     = {187-199},
  year      = {2016}}

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