Exponential deepening A∗ for real-time agent-centered search

Guni Sharon, Ariel Felner, Nathan R. Sturtevant

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

12 Scopus citations

Abstract

In the Real-Time Agent-Centered Search (RTACS) problem, an agent has to arrive at a goal location while acting and reasoning in the physical world. Traditionally, RTACS problems are solved by propagating and updating heuristic values of states visited by the agent. In existing RTACS algorithms the agent may revisit each state many times causing the entire procedure to be quadratic in the state space. We study the Iterative Deepening (ID) approach for solving RTACS and introduce Exponential Deepening A∗ (EDA∗), an RTACS algorithm where the threshold between successive Depth-First calls is increased exponentially. EDA∗ is proven to hold a worst case bound that is linear in the state space. Experimental results supporting this bound are presented and demonstrate up to 10x reduction over existing RTACS solvers wrt distance traveled, states expanded and CPU runtime.

Original languageEnglish
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAI Access Foundation
Pages871-877
Number of pages7
ISBN (Electronic)9781577356783
StatePublished - 1 Jan 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: 27 Jul 201431 Jul 2014

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Conference

Conference28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
Country/TerritoryCanada
CityQuebec City
Period27/07/1431/07/14

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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