3 papers
cs.AI2026
Scaling Observation-aware Planning in Uncertain Domains
Adrian Zvizdenco, Arthur Conrado Veiga Bosquetti, Alberto Lluch Lafuente +1
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 cos…
cs.PL2026
Caesar: A Deductive Verifier for Probabilistic Programs
Philipp Schröer, Kevin Batz, Umut YiÄit Dural +4
Caesar is a deductive verifier for probabilistic programs. At its core lies HeyVL, a quantitative intermediate verification language based on the real-valued logic HeyLo. HeyVL all…
cs.LO2024
J-P: MDP. FP. PP.: Characterizing Total Expected Rewards in Markov Decision Processes as Least Fixed Points with an Application to Operational Semantics of Probabilistic Programs (Technical Report)
Kevin Batz, Benjamin Lucien Kaminski, Christoph Matheja +1
Markov decision processes (MDPs) with rewards are a widespread and well-studied model for systems that make both probabilistic and nondeterministic choices. A fundamental result ab…