東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Real-Time Planning as Decision-Makin...

Mitchell, Andrew.

Linked to FindBook

Google Book

Amazon

博客來

Real-Time Planning as Decision-Making under Uncertainty.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Real-Time Planning as Decision-Making under Uncertainty./
Author:	Mitchell, Andrew.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	55 p.
Notes:	Source: Masters Abstracts International, Volume: 58-04.
Contained By:	Masters Abstracts International58-04(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13422108
ISBN:	9780438824119

Real-Time Planning as Decision-Making under Uncertainty.
Mitchell, Andrew.

Real-Time Planning as Decision-Making under Uncertainty. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 55 p.

Source: Masters Abstracts International, Volume: 58-04.

Thesis (M.S.)--University of New Hampshire, 2018.

In real-time planning, an agent must select the next action to take within a fixed time bound. Many popular real-time heuristic search methods approach this by expanding nodes using time-limited A* and selecting the action leading toward the frontier node with the lowest f value. In this thesis, we reconsider real-time planning as a problem of decision-making under uncertainty. We treat heuristic values as uncertain evidence and we explore several backup methods for aggregating this evidence. We then propose a novel lookahead strategy that expands nodes to minimize risk, the expected regret in case a non-optimal action is chosen. We evaluate these methods in a simple synthetic benchmark and the sliding tile puzzle and find that they outperform previous methods. This work illustrates how uncertainty can arise even when solving deterministic planning problems, due to the inherent ignorance of time-limited search algorithms about those portions of the state space that they have not computed, and how an agent can benefit from explicitly meta-reasoning about this uncertainty.

ISBN: 9780438824119Subjects--Topical Terms:

523869
Computer science.

Real-Time Planning as Decision-Making under Uncertainty.
LDR:01984nmm a2200301 4500 001 2203287
005 20190528072656.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438824119
035 $a (MiAaPQ)AAI13422108
035 $a (MiAaPQ)unh:10782
035 $a AAI13422108
040 $a MiAaPQ $c MiAaPQ
100 1 $a Mitchell, Andrew. $3 3379855
245 1 0 $a Real-Time Planning as Decision-Making under Uncertainty.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 55 p.
500 $a Source: Masters Abstracts International, Volume: 58-04.
500 $a Adviser: Wheeler Ruml.
502 $a Thesis (M.S.)--University of New Hampshire, 2018.
520 $a In real-time planning, an agent must select the next action to take within a fixed time bound. Many popular real-time heuristic search methods approach this by expanding nodes using time-limited A* and selecting the action leading toward the frontier node with the lowest f value. In this thesis, we reconsider real-time planning as a problem of decision-making under uncertainty. We treat heuristic values as uncertain evidence and we explore several backup methods for aggregating this evidence. We then propose a novel lookahead strategy that expands nodes to minimize risk, the expected regret in case a non-optimal action is chosen. We evaluate these methods in a simple synthetic benchmark and the sliding tile puzzle and find that they outperform previous methods. This work illustrates how uncertainty can arise even when solving deterministic planning problems, due to the inherent ignorance of time-limited search algorithms about those portions of the state space that they have not computed, and how an agent can benefit from explicitly meta-reasoning about this uncertainty.
590 $a School code: 0141.
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
690 $a 0984
690 $a 0800
710 2 $a University of New Hampshire. $b Computer Science. $3 3430079
773 0 $t Masters Abstracts International $g 58-04(E).
790 $a 0141
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13422108