1 citations · 1 across the 6 of their papers we have counts for
8 papers
A Unifying Framework of Concept-based Explainable AI with Completeness Guarantees
Vojtěch Kůr, Adam Kukučka, Tomáš Brázdil +1
Concept-based explanations describe neural network predictions through human-understandable properties of inputs called concepts. The field encompasses approaches that differ in ho…
Multiagent Stochastic Shortest Path Problem
Martin Jonáš, Antonín Kučera, Vojtěch Kůr +2
We introduce and study the multi-agent stochastic shortest path (MSSP) problem, in which agents strive to reach a target state, aiming to minimize the expected time to reach th…
LLEXICORP: End-user Explainability of Convolutional Neural Networks
Vojtěch Kůr, Adam Bajger, Adam Kukučka +3
Convolutional neural networks (CNNs) underpin many modern computer vision systems. With applications ranging from common to critical areas, a need to explain and understand the mod…
Explaining Digital Pathology Models via Clustering Activations
Adam Bajger, Jan Obdržálek, Vojtěch Kůr +4
We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks. Unlike commonly used methods based on saliency maps, suc…
Steady-State Strategy Synthesis for Swarms of Autonomous Agents
Martin Jonáš, Antonín Kučera, Vojtěch Kůr +1
Steady-state synthesis aims to construct a policy for a given MDP such that the long-run average frequencies of visits to the vertices of satisfy given numerical constraint…
Memory Assignment for Finite-Memory Strategies in Adversarial Patrolling Games
Vojtěch Kůr, Vít Musil, Vojtěch Řehák
Adversarial Patrolling games form a subclass of Security games where a Defender moves between locations, guarding vulnerable targets. The main algorithmic problem is constructing a…
Multiple Mean-Payoff Optimization under Local Stability Constraints
David Klaška, Antonín Kučera, Vojtěch Kůr +2
The long-run average payoff per transition (mean payoff) is the main tool for specifying the performance and dependability properties of discrete systems. The problem of constructi…
Optimizing Local Satisfaction of Long-Run Average Objectives in Markov Decision Processes
David Klaška, Antonín Kučera, Vojtěch Kůr +2
Long-run average optimization problems for Markov decision processes (MDPs) require constructing policies with optimal steady-state behavior, i.e., optimal limit frequency of visit…