6 citations · 6 across the 7 of their papers we have counts for
5 papers · 1 filter
Domain-Independent Dynamic Programming with Constraint Propagation
Imko Marijnissen, J. Christopher Beck, Emir Demirović +1
There are two prevalent model-based paradigms for combinatorial problems: 1) state-based representations, such as heuristic search, dynamic programming (DP), and decision diagrams,…
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic Programming
Minori Narita, Ryo Kuroiwa, J. Christopher Beck
Domain-Independent Dynamic Programming (DIDP) is a state-space search paradigm based on dynamic programming for combinatorial optimization. In its current implementation, DIDP guid…
Domain-Independent Dynamic Programming
Ryo Kuroiwa, J. Christopher Beck
For combinatorial optimization problems, model-based paradigms such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a proble…
Learning Search-Space Specific Heuristics Using Neural Networks
Yu Liu, Ryo Kuroiwa, Alex Fukunaga
We propose and evaluate a system which learns a neuralnetwork heuristic function for forward search-based, satisficing classical planning. Our system learns distance-to-goal estima…
Domain-Independent Dynamic Programming: Generic State Space Search for Combinatorial Optimization
Ryo Kuroiwa, J. Christopher Beck
For combinatorial optimization problems, model-based approaches such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a probl…