6 papers
Neurosymbolic Conformal Classification
Arthur Ledaguenel, Céline Hudelot, Mostepha Khouadjia
The last decades have seen a drastic improvement of Machine Learning (ML), mainly driven by Deep Learning (DL). However, despite the resounding successes of ML in many domains, the…
A Complexity Map of Probabilistic Reasoning for Neurosymbolic Classification Techniques
Arthur Ledaguenel, Céline Hudelot, Mostepha Khouadjia
Neurosymbolic artificial intelligence is a growing field of research aiming to combine neural network learning capabilities with the reasoning abilities of symbolic systems. Inform…
Improving Neural-based Classification with Logical Background Knowledge
Arthur Ledaguenel, Céline Hudelot, Mostepha Khouadjia
Neurosymbolic AI is a growing field of research aiming to combine neural networks learning capabilities with the reasoning abilities of symbolic systems. This hybridization can tak…
Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning
Mostepha Redouane Khouadjia, Marc Schoenauer, Vincent Vidal +2
Parameter tuning is recognized today as a crucial ingredient when tackling an optimization problem. Several meta-optimization methods have been proposed to find the best parameter…
Multi-Objective AI Planning: Comparing Aggregation and Pareto Approaches
Mostepha Redouane Khouadjia, Marc Schoenauer, Vincent Vidal +2
Most real-world Planning problems are multi-objective, trying to minimize both the makespan of the solution plan, and some cost of the actions involved in the plan. But most, if no…
Multi-Objective AI Planning: Evaluating DAE-YAHSP on a Tunable Benchmark
Mostepha Redouane Khouadjia, Marc Schoenauer, Vincent Vidal +2
All standard AI planners to-date can only handle a single objective, and the only way for them to take into account multiple objectives is by aggregation of the objectives. Further…