| Full text | |
| Author(s): |
Total Authors: 2
|
| Affiliation: | [1] Univ Sao Paulo, Inst Matemat & Estat, BR-05508090 Sao Paulo - Brazil
[2] Univ Sao Paulo, Escola Politecn, BR-05508030 Sao Paulo - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF APPROXIMATE REASONING; v. 68, p. 230-245, JAN 2016. |
| Web of Science Citations: | 0 |
| Abstract | |
Limited memory influence diagrams are graph-based models that describe decision problems with limited information such as planning with teams and/or agents with imperfect recall. Solving a (limited memory) influence diagram is an NP-hard problem, often approached through local search. In this work we give a closer look at k-neighborhood local search approaches. We show that finding a k-neighboring strategy that improves on the current solution is W{[}1]-hard and hence unlikely to be polynomial-time tractable. We also show that finding a strategy that resembles an optimal strategy (but may have low expected utility) is NP-hard. We then develop fast schema to perform approximate k-local search; experiments show that our methods improve on current local search algorithms both with respect to time and to accuracy. (C) 2015 Elsevier Inc. All rights reserved. (AU) | |
| FAPESP's process: | 13/23197-4 - Efficient algorithms for graph-based decision making under uncertainty |
| Grantee: | Denis Deratani Mauá |
| Support Opportunities: | Scholarships in Brazil - Post-Doctorate |