Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866917377347682304 |
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| author | Speziali, Paolo De Greef, Arno Asadi, Mehrdad Röpke, Willem Nowé, Ann Roijers, Diederik M. |
| author_facet | Speziali, Paolo De Greef, Arno Asadi, Mehrdad Röpke, Willem Nowé, Ann Roijers, Diederik M. |
| contents | We propose the Preference Guided Iterated Pareto Referent Optimisation (PG-IPRO) for urban route planning for people with different accessibility requirements and preferences. With this algorithm the user can interact with the system by giving feedback on a route, i.e., the user can say which objective should be further minimized, or conversely can be relaxed. This leads to intuitive user interaction, that is especially effective during early iterations compared to information-gain-based interaction. Furthermore, due to PG-IPRO's iterative nature, the full set of alternative, possibly optimal policies (the Pareto front), is never computed, leading to higher computational efficiency and shorter waiting times for users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00795 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning Speziali, Paolo De Greef, Arno Asadi, Mehrdad Röpke, Willem Nowé, Ann Roijers, Diederik M. Artificial Intelligence Machine Learning We propose the Preference Guided Iterated Pareto Referent Optimisation (PG-IPRO) for urban route planning for people with different accessibility requirements and preferences. With this algorithm the user can interact with the system by giving feedback on a route, i.e., the user can say which objective should be further minimized, or conversely can be relaxed. This leads to intuitive user interaction, that is especially effective during early iterations compared to information-gain-based interaction. Furthermore, due to PG-IPRO's iterative nature, the full set of alternative, possibly optimal policies (the Pareto front), is never computed, leading to higher computational efficiency and shorter waiting times for users. |
| title | Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.00795 |