MADMAX is a Haskell-embedded DSL for multi-attribute, multi-layered decision making. An important feature of this DSL is the ability to generate explanations of why a computed optimal solution is better than its alternatives.
The functional approach and Haskell's type system support a high-level formulation of decision-making problems, which facilitates a number of innovations, including the gradual evolution and adaptation of problem representations, a more user-friendly form of sensitivity analysis based on problem domain data, and fine-grained control over explanations.
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This content will become publicly available on June 1, 2025
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
- NSF-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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