The paper introduces the Orienteering Problem with Uncertain Time‑Varying Rewards (OP‑UTVR), a new variant of the classic orienteering problem that allows agents to estimate and forecast reward dynamics from observations. Three planners with different planning horizons and online adaptivity are proposed, and theoretical performance bounds under reward stochasticity are derived. A mobile service robot benchmark is presented, and experiments show trade‑offs between planning horizon and adaptivity, highlighting the benefits of long‑horizon planning with online adaptation.
arXiv:2606. 18514v1 Announce Type: cross Abstract: Neural combinatorial optimization (NCO) offers a promising alternative to traditional heuristic-based methods for solving complex graph optimization problems by proposing to learn heuristics through data.
arXiv:2607. 13546v1 Announce Type: new Abstract: Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sensing.
arXiv:2606. 19328v1 Announce Type: cross Abstract: Preference-based RL provides an approach to learning reward models from pairwise comparisons of behaviors, bypassing the need for explicit reward design.
arXiv:2602. 17315v3 Announce Type: replace-cross Abstract: We introduce Flickering Multi-Armed Bandits (FMAB) to model sequential decision-making in environments with changing action availability, where accessibility of the next action is restricted to a subset dependent on the agent's current choice.
Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sensing. A fundamental challenge is online worker recruitment under uncertainty, where the platform must learn workers' sensing performance while operating with a limited budget.
arXiv:2607. 20289v1 Announce Type: cross Abstract: We consider a task planning scenario in which robots sharing a persistent environment are assigned tasks one at a time from a held-out sequence.
arXiv:2607. 15552v1 Announce Type: cross Abstract: Generating personalized trip itineraries is a complex planning task and involves a tension between hard combinatorial feasibility and soft latent desirability.
Finding policies for Markov Decision Processes (MDPs) is a central problem in areas such as Reinforcement Learning and Operations Research. Here, we have to repeatedly choose an action that should be...
arXiv:2609.15539v1 Announce Type: cross Abstract: Finding policies for Markov Decision Processes (MDPs) is a central problem in areas such as Reinforcement Learning and Operations Research. Here, we...
arXiv:2603. 23800v2 Announce Type: replace-cross Abstract: We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known environments.
arXiv:2608. 07751v1 Announce Type: cross Abstract: Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors.
arXiv:2609.05519v1 Announce Type: cross Abstract: We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points...