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Found 6 papers, 0 papers with code
Preference-Based Planning in Stochastic Environments: From Partially-Ordered Temporal Goals to Most Preferred Policies
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.
Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives
We demonstrate the efficacy and applicability of the logic and the algorithm on several case studies with detailed analyses for each.
Synthesis of Opacity-Enforcing Winning Strategies Against Colluded Opponent
Furthermore, if such a strategy does not exist, winning for P1 must entail the price of revealing his secret to the observer.
Probabilistic Planning with Partially Ordered Preferences over Temporal Goals
We prove that a weak-stochastic nondominated policy given the preference specification is Pareto-optimal in the constructed multi-objective MDP, and vice versa.
Accelerating combinatorial filter reduction through constraints
Reduction of combinatorial filters involves compressing state representations that robots use.
What to Do When You Can't Do It All: Temporal Logic Planning with Soft Temporal Logic Constraints
In this paper, we consider a temporal logic planning problem in which the objective is to find an infinite trajectory that satisfies an optimal selection from a set of soft specifications expressed in linear temporal logic (LTL) while nevertheless satisfying a hard specification expressed in LTL.
