Scaling Observation-aware Planning in Uncertain Domains
Deciding which sensing capabilities to deploy on an agent in uncertain domains is a fundamental engineering challenge, in which one balances task achievability against the high costs of hardware and processing. This problem has previously been formalized as the Optimal Observability Problem (OOP), based on the well-known Partially Observable Markov Decision Process (POMDP) model for decision-making. This work studies (sub-)symbolic techniques to scale solving of decidable fragments of the OOP, namely the Sensor Selection Problem (SSP) and the Positional Observability Problem (POP). Besides improving the original approach based on parameter synthesis, we develop a new solving method that i...
| Authors | Adrian Zvizdenco, Arthur Conrado Veiga Bosquetti, Alberto Lluch Lafuente et al. |
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| Primary category | cs.AI |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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