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ABSTRACT Transparency in computing is an important precondition to ensure the trust of users. One concrete way of delivering transparency is to provide explanations of computing results. To this end, we introduce a method for explaining the results of various linear and hierarchical multi‐criteria decision‐making (MCDM) techniques such as the weighted sum model (WSM) and the analytic hierarchy process (AHP). The two key ideas are (A) to maintain a fine‐grained representation of the values manipulated by these techniques and (B) to derive explanations from these representations through merging, filtering, and aggregating operations. An explanation in our model presents a high‐level comparison of two alternatives in an MCDM problem, presumably an optimal and a non‐optimal one, illuminating why one alternative was preferred over the other. We show the usefulness of our techniques by generating explanations for two well‐known examples from the MCDM literature. Finally, we show their efficacy by performing computational experiments.
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Explanations for combinatorial optimization problems
We introduce a representation for generating explanations for the outcomes of combinatorial optimization algorithms. The two key ideas are (A) to maintain fine-grained representations of the values manipulated by these algorithms and (B) to derive explanations from these representations through merge, filter, and aggregation operations. An explanation in our model presents essentially a high-level comparison of the solution to a problem with a hypothesized alternative, illuminating why the solution is better than the alternative. Our value representation results in explanations smaller than other dynamic program representations, such as traces. Based on a measure for the conciseness of explanations we demonstrate through a number of experiments that the explanations produced by our approach are small and scale well with problem size across a number of different applications.
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- Award ID(s):
- 2114642
- PAR ID:
- 10527161
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Journal of Computer Languages
- Volume:
- 79
- Issue:
- C
- ISSN:
- 2590-1184
- Page Range / eLocation ID:
- 101272
- Subject(s) / Keyword(s):
- Contrastive explanation, Explanation simplification
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.cola.2024.101272
