Abstract
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.
Original language | English (US) |
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Title of host publication | Sequential Decision Making for Intelligent Agents - Papers from the AAAI 2015 Fall Symposium, Technical Report |
Publisher | AI Access Foundation |
Pages | 83-90 |
Number of pages | 8 |
Volume | FS-15-06 |
ISBN (Electronic) | 9781577357520 |
State | Published - Jan 1 2015 |
Externally published | Yes |
Event | AAAI 2015 Fall Symposium - Arlington, United States Duration: Nov 12 2015 → Nov 14 2015 |
Other
Other | AAAI 2015 Fall Symposium |
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Country/Territory | United States |
City | Arlington |
Period | 11/12/15 → 11/14/15 |
ASJC Scopus subject areas
- Engineering(all)