Search Results for author: Abhishek N. Kulkarni

Found 5 papers, 0 papers with code

Preference-Based Planning in Stochastic Environments: From Partially-Ordered Temporal Goals to Most Preferred Policies

no code implementations27 Mar 2024 Hazhar Rahmani, Abhishek N. Kulkarni, Jie Fu

In the second step, we prove that finding a most preferred policy is equivalent to computing a Pareto-optimal policy in a multi-objective MDP that is constructed from the original MDP, the preference automaton, and the chosen stochastic ordering relation.

Synthesis of Opacity-Enforcing Winning Strategies Against Colluded Opponent

no code implementations3 Apr 2023 Chongyang Shi, Abhishek N. Kulkarni, Hazhar Rahmani, Jie Fu

Furthermore, if such a strategy does not exist, winning for P1 must entail the price of revealing his secret to the observer.

Motion Planning

Opportunistic Qualitative Planning in Stochastic Systems with Incomplete Preferences over Reachability Objectives

no code implementations4 Oct 2022 Abhishek N. Kulkarni, Jie Fu

We construct a model called an improvement MDP, in which the synthesis of SPI and SASI strategies that guarantee at least one improvement reduces to computing positive and almost-sure winning strategies in an MDP.

Motion Planning

Probabilistic Planning with Partially Ordered Preferences over Temporal Goals

no code implementations25 Sep 2022 Hazhar Rahmani, Abhishek N. Kulkarni, Jie Fu

We prove that a weak-stochastic nondominated policy given the preference specification is Pareto-optimal in the constructed multi-objective MDP, and vice versa.

A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception

no code implementations7 Aug 2020 Abhishek N. Kulkarni, Jie Fu

Given qualitative security specifications in formal logic, we show that the solution concepts from hypergames and reactive synthesis in formal methods can be extended to synthesize effective dynamic defense strategy using cyber deception.

Formal Logic

Cannot find the paper you are looking for? You can Submit a new open access paper.