TY - GEN
T1 - Metaphysics of planning domain descriptions
AU - Srivastava, Siddharth
AU - Russell, Stuart
AU - Pinto, Alessandro
N1 - Publisher Copyright:
© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2015
Y1 - 2015
N2 - Domain models for sequential decision-making typically represent abstract versions of real-world systems. In practice, such abstract representations are compact, easy to maintain, and afford faster solution times. Unfortunately, as we show in the paper, simple ways of abstracting solvable real-world problems may lead to models whose solutions are incorrect with respect to the real-world problem. There is some evidence that such limitations have restricted the applicability of sequential decision-making technology in the real world, as is apparent in the case of task and motion planning in robotics. We show that the situation can be ameliorated by a combination of increased expressive power-for example, allowing angelic nondeterminism in action effects-and new kinds of algorithmic approaches designed to produce correct solutions from initially incorrect or non-Markovian abstract models.
AB - Domain models for sequential decision-making typically represent abstract versions of real-world systems. In practice, such abstract representations are compact, easy to maintain, and afford faster solution times. Unfortunately, as we show in the paper, simple ways of abstracting solvable real-world problems may lead to models whose solutions are incorrect with respect to the real-world problem. There is some evidence that such limitations have restricted the applicability of sequential decision-making technology in the real world, as is apparent in the case of task and motion planning in robotics. We show that the situation can be ameliorated by a combination of increased expressive power-for example, allowing angelic nondeterminism in action effects-and new kinds of algorithmic approaches designed to produce correct solutions from initially incorrect or non-Markovian abstract models.
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M3 - Conference contribution
AN - SCOPUS:84964595259
T3 - AAAI Fall Symposium - Technical Report
SP - 83
EP - 90
BT - Sequential Decision Making for Intelligent Agents - Papers from the AAAI 2015 Fall Symposium, Technical Report
PB - AI Access Foundation
T2 - AAAI 2015 Fall Symposium
Y2 - 12 November 2015 through 14 November 2015
ER -