Deep model-based reinforcement learning for high-dimensional problems, a survey
Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems have been solved in tasks such as game …
complex sequential decision making problems have been solved in tasks such as game …
[图书][B] Constraint solving and planning with Picat
NF Zhou, H Kjellerstrand, J Fruhman - 2015 - Springer
Many complex systems, ranging from social, industrial, economics, financial, educational, to
military, require that we obtain high-quality solutions to combinatorial problems. Linear …
military, require that we obtain high-quality solutions to combinatorial problems. Linear …
Planning as tabled logic programming
This paper describes Picat's planner, its implementation, and planning models for several
domains used in International Planning Competition (IPC) 2014. Picat's planner is …
domains used in International Planning Competition (IPC) 2014. Picat's planner is …
Keke AI Competition: Solving puzzle levels in a dynamically changing mechanic space
M Charity, J Togelius - 2022 IEEE Conference on Games (CoG), 2022 - ieeexplore.ieee.org
The Keke AI Competition introduces an artificial agent competition for the game Baba is You-
a Sokoban-like puzzle game where players can create rules that influence the mechanics of …
a Sokoban-like puzzle game where players can create rules that influence the mechanics of …
Deep reinforcement learning, a textbook
A Plaat - arXiv preprint arXiv:2201.02135, 2022 - arxiv.org
Deep reinforcement learning has gathered much attention recently. Impressive results were
achieved in activities as diverse as autonomous driving, game playing, molecular …
achieved in activities as diverse as autonomous driving, game playing, molecular …
On modeling planning problems in tabled logic programming
Current research in planning focuses mainly on so called domain independent models
using the Planning Domain Description Language (PDDL) as the domain modeling …
using the Planning Domain Description Language (PDDL) as the domain modeling …
Using tabled logic programming to solve the Petrobras planning problem
R Barták, NF Zhou - Theory and Practice of Logic Programming, 2014 - cambridge.org
Tabling has been used for some time to improve efficiency of Prolog programs by
memorizing answered queries. The same idea can be naturally used to memorize visited …
memorizing answered queries. The same idea can be naturally used to memorize visited …
Exploring ILASP Through Logic Puzzles Modelling
T Dreossi - CEUR WORKSHOP PROCEEDINGS, 2023 - air.uniud.it
ILASP (Inductive Learning of Answer Set Programs) is a logic-based machine learning
system. It makes use of existing knowledge base, containing anything known before the …
system. It makes use of existing knowledge base, containing anything known before the …
Modeling and solving the rush hour puzzle
We introduce the physical puzzle Rush Hour and its generalization. We briefly survey its
complexity limits, then we model and solve it using declarative paradigms. In particular, we …
complexity limits, then we model and solve it using declarative paradigms. In particular, we …
On Grid Graph Reachability and Puzzle Games
Many puzzle video games, like Sokoban, involve moving some agent in a maze. The
reachable locations are usually apparent for a human player, and the difficulty of the game is …
reachable locations are usually apparent for a human player, and the difficulty of the game is …