Search for Choquet-optimal paths under uncertainty
This work addresses robust path-planning under uncertainty for applications in decision theory, but it appears incremental as it applies an existing criterion to a specific problem with new algorithms.
The paper tackles the problem of finding robust paths in uncertain environments using Choquet expected utility (CEU) as a decision criterion, proposing two heuristic search algorithms that demonstrate practical efficiency in numerical experiments.
Choquet expected utility (CEU) is one of the most sophisticated decision criteria used in decision theory under uncertainty. It provides a generalisation of expected utility enhancing both descriptive and prescriptive possibilities. In this paper, we investigate the use of CEU for path-planning under uncertainty with a special focus on robust solutions. We first recall the main features of the CEU model and introduce some examples showing its descriptive potential. Then we focus on the search for Choquet-optimal paths in multivalued implicit graphs where costs depend on different scenarios. After discussing complexity issues, we propose two different heuristic search algorithms to solve the problem. Finally, numerical experiments are reported, showing the practical efficiency of the proposed algorithms.