Abstract
Computer simulations have been carried out to investigate the performance
of two measures for abductive inference, Maximum Likelihood (ML),
and Product Coherence Measure (PCM), by comparing them with a third
approach, Most Probable Explanation (MPE). These have been realized through
experiments that compare outcomes from a specified model (the correct model)
with those from incorrect models which assume that the hypotheses are mutually exclusive or independent. The results show that PCM tracks the results of MPE more closely than ML when the degree of competition is greater than 0 and hence is able to infer explanations that are more likely to be true under such a condition. Experiments on the robustness of the measures with respect to
incorrect model assumptions show that ML is more robust in general, but that
MPE and PCM are more robust when the degree of competition is positive. The
results also show that in general it is more reasonable to assume the hypotheses
in question are independent than to assume they are mutually exclusive.
of two measures for abductive inference, Maximum Likelihood (ML),
and Product Coherence Measure (PCM), by comparing them with a third
approach, Most Probable Explanation (MPE). These have been realized through
experiments that compare outcomes from a specified model (the correct model)
with those from incorrect models which assume that the hypotheses are mutually exclusive or independent. The results show that PCM tracks the results of MPE more closely than ML when the degree of competition is greater than 0 and hence is able to infer explanations that are more likely to be true under such a condition. Experiments on the robustness of the measures with respect to
incorrect model assumptions show that ML is more robust in general, but that
MPE and PCM are more robust when the degree of competition is positive. The
results also show that in general it is more reasonable to assume the hypotheses
in question are independent than to assume they are mutually exclusive.
| Original language | English |
|---|---|
| Title of host publication | Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings |
| Editors | Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager |
| Publisher | Springer |
| Chapter | 3 |
| Pages | 304-317 |
| Number of pages | 14 |
| Volume | 1239 |
| ISBN (Electronic) | 978-3-030-50153-2 |
| ISBN (Print) | 978-3-030-50152-5 |
| DOIs | |
| Publication status | Published online - 5 Jun 2020 |
| Event | 18th International Conference, IPMU 2020 Lisbon, Portugal, June 15–19, 2020 : IPMU 2020 - Lisbon, Portugal Duration: 17 Jun 2020 → 19 Jun 2020 Conference number: 18th https://ipmu2020.inesc-id.pt/?page_id=806 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Volume | 1239 CCIS |
| ISSN (Print) | 1865-0929 |
| ISSN (Electronic) | 1865-0937 |
Conference
| Conference | 18th International Conference, IPMU 2020 Lisbon, Portugal, June 15–19, 2020 |
|---|---|
| Country/Territory | Portugal |
| City | Lisbon |
| Period | 17/06/20 → 19/06/20 |
| Internet address |
Keywords
- Inference to the Best Explanation (IBE)
- Explanatory reasoning
- Hypotheses competition
- Abduction