3 citations · 4 across the 7 of their papers we have counts for
7 papers
Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives
Lening Li, Hazhar Rahmani, Jie Fu
This paper studies temporal planning in probabilistic environments, modeled as labeled Markov decision processes (MDPs), with user preferences over multiple temporal goals. Existin…
Topology-Guided Modular Actor-Critic Learning for Continuous Systems under Temporal Objectives
Lening Li, Zhentian Qian, Jianan Xia +7
We study formal policy synthesis for continuous-state stochastic systems under linear temporal logic specifications. The product of the system with the automaton of the specificati…
Synthesis of Proactive Sensor Placement In Probabilistic Attack Graphs
Lening Li, Haoxiang Ma, Shuo Han +1
This paper studies the deployment of joint moving target defense (MTD) and deception against multi-stage cyberattacks. Given the system equipped with MTD that randomizes between di…
Policy Synthesis for Metric Interval Temporal Logic with Probabilistic Distributions
Lening Li, Jie Fu
Metric Temporal Logic can express temporally evolving properties with time-critical constraints or time-triggered constraints for real-time systems. This paper extends the Metric I…
Dynamic Hypergames for Synthesis of Deceptive Strategies with Temporal Logic Objectives
Lening Li, Haoxiang Ma, Abhishek N. Kulkarni +1
In this paper, we study the use of deception for strategic planning in adversarial environments. We model the interaction between the agent (player 1) and the adversary (player 2)…
Topological Approximate Dynamic Programming under Temporal Logic Constraints
Lening Li, Jie Fu
In this paper, we develop a Topological Approximate Dynamic Programming (TADP) method for planningin stochastic systems modeled as Markov Decision Processesto maximize the probabil…
Approximate Dynamic Programming with Probabilistic Temporal Logic Constraints
Lening Li, Jie Fu
In this paper, we develop approximate dynamic programming methods for stochastic systems modeled as Markov Decision Processes, given both soft performance criteria and hard constra…