activity
20242026
collaborators

7 papers

cs.AI2026

Solving Robust POMDPs with Omega-regular Objectives via Partially Observable Stochastic Games

Durgam Latha, Dion Reji, S. Akshay +3

Robust POMDPs (RPOMDPs) generalize classical POMDPs to the setting where exact transition probabilities are not known -- rather, they are only known to belong to some uncertainty s…

cs.AI2026

Entropy Objectives in Markov Decision Processes

S. Akshay, Raghav Goyal, Aditya Neeraje +1

We consider the problem of synthesizing control policies that enforce a concentration property on the state distributions of a stochastic system. We present a formalization of this…

cs.AI2026

Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach

Ajinkya Naik, Chaitanya Garg, S. Akshay +2

Decision tree ensembles (DTE) are a popular model for a wide range of AI classification tasks, used in multiple safety critical domains, and hence verifying properties on these mod…

cs.PL2026

Program Synthesis for Non-Linear Real Arithmetic: Going Beyond Realizability

S. Akshay, Supratik Chakraborty, R. Govind +1

We study the problem of synthesizing programs from nonlinear real arithmetic (NRA) specifications. Existing techniques, such as syntax-guided synthesis (SyGuS), fail to synthesize…

cs.LO2026

Knowledge Compilation for Quantification in Alternating Automata

S. Akshay, Alfredo Cantarella, Supratik Chakraborty +2

We present a knowledge compilation approach for existential and universal quantification in alternating automata. Knowledge compilation transforms formulas into normal forms with s…

cs.LO2025

Omega-regular Verification and Control for Distributional Specifications in MDPs

S. Akshay, Ouldouz Neysari, Đorđe Žikelić

A classical approach to studying Markov decision processes (MDPs) is to view them as state transformers. However, MDPs can also be viewed as distribution transformers, where an MDP…

cs.AI2024

Certified Policy Verification and Synthesis for MDPs under Distributional Reach-avoidance Properties

S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer +1

Markov Decision Processes (MDPs) are a classical model for decision making in the presence of uncertainty. Often they are viewed as state transformers with planning objectives defi…