Showing cs.AIShow all
3 papers · 1 filter
cs.AI2026
POMDPPlanners: Open-Source Package for POMDP Planning
Yaacov Pariente, Vadim Indelman
We present POMDPPlanners, an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process (POMDP) planning algorithms. The package integrates…
cs.AI2026
Online Risk-Averse Planning in POMDPs Using Iterated CVaR Value Function
Yaacov Pariente, Vadim Indelman
We study risk-sensitive planning under partial observability using the dynamic risk measure Iterated Conditional Value-at-Risk (ICVaR). A policy evaluation algorithm for ICVaR is d…
cs.AI2024
Simplification of Risk Averse POMDPs with Performance Guarantees
Yaacov Pariente, Vadim Indelman
Risk averse decision making under uncertainty in partially observable domains is a fundamental problem in AI and essential for reliable autonomous agents. In our case, the problem…