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Found 10 papers, 4 papers with code
Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning
In an evaluation with twelve scientists, we found that Cieran effectively modeled user preferences to rank colormaps and leveraged this model to create new quality designs.
Feasibility-Guided Safety-Aware Model Predictive Control for Jump Markov Linear Systems
In this paper, we present a framework that synthesizes maximally safe control policies for Jump Markov Linear Systems subject to stochastic mode switches.
Recursively-Constrained Partially Observable Markov Decision Processes
Evaluations on a set of benchmark problems demonstrate the efficacy of our algorithm and show that policies for RC-POMDPs produce more desirable behaviors than policies for C-POMDPs.
Explanation through Reward Model Reconciliation using POMDP Tree Search
As artificial intelligence (AI) algorithms are increasingly used in mission-critical applications, promoting user-trust of these systems will be essential to their success.
Sampling-based Reactive Synthesis for Nondeterministic Hybrid Systems
This paper introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints.
Optimality Guarantees for Particle Belief Approximation of POMDPs
Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces.
Automaton-Guided Control Synthesis for Signal Temporal Logic Specifications
This paper presents an algorithmic framework for control synthesis of continuous dynamical systems subject to signal temporal logic (STL) specifications.
Compositional Learning-based Planning for Vision POMDPs
The Partially Observable Markov Decision Process (POMDP) is a powerful framework for capturing decision-making problems that involve state and transition uncertainty.
Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs
This paper introduces Voronoi Progressive Widening (VPW), a generalization of Voronoi optimistic optimization (VOO) and action progressive widening to partially observable Markov decision processes (POMDPs).
Sparse tree search optimality guarantees in POMDPs with continuous observation spaces
Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve.
