Name
Abstract | ||
|---|---|---|
Min-based possibilistic influence diagrams offer a compact modeling of decision problems under uncertainty. Uncertainty and preferential relations are expressed on the same structure by using ordinal data. In many applications, it may be natural to represent expert knowledge and preferences separately and treat all nodes similarly. This work shows how an influence diagram can be equivalently represented by two possibilistic networks: the first one represents knowledge of an agent and the second one represents agent's preferences. Thus, the decision evaluation process is based on more compact possibilistic network. Then, we show that the computation of sequential optimal decisions (strategy) comes down to compute a normalization degree of the junction tree associated with the graph representing the fusion of agents beliefs and its preferences resulting from the proposed decomposition process. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-53354-4_10 | AGENTS AND ARTIFICIAL INTELLIGENCE, ICAART 2016 |
Field | DocType | Volume |
Graph,Decision problem,Normalization (statistics),Computer science,Ordinal data,Influence diagram,Artificial intelligence,Machine learning,Computation | Conference | 10162 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salem Benferhat | 1 | 2585 | 216.23 |
| Khaoula Boutouhami | 2 | 1 | 1.71 |
| Hadja Faiza Khellaf-Haned | 3 | 0 | 0.34 |
| Ismahane Zeddigha | 4 | 2 | 2.45 |