Transpath: Learning heuristics for grid-based pathfinding via transformers
Heuristic search algorithms, eg A*, are the commonly used tools for pathfinding on grids, ie
graphs of regular structure that are widely employed to represent environments in robotics …
graphs of regular structure that are widely employed to represent environments in robotics …
Generative models for grid-based and image-based pathfinding
Pathfinding is a challenging problem which generally asks to find a sequence of valid moves
for an agent provided with a representation of the environment, ie a map, in which it …
for an agent provided with a representation of the environment, ie a map, in which it …
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
VA Darvariu, S Hailes, M Musolesi - arxiv preprint arxiv:2404.06492, 2024 - arxiv.org
Graphs are a natural representation for systems based on relations between connected
entities. Combinatorial optimization problems, which arise when considering an objective …
entities. Combinatorial optimization problems, which arise when considering an objective …
[HTML][HTML] NERO: NEural algorithmic reasoning for zeRO-day attack detection in the IoT: A hybrid approach
Anomaly detection approaches for network intrusion detection learn to identify deviations
from normal behavior on a data-driven basis. However, current approaches strive to infer the …
from normal behavior on a data-driven basis. However, current approaches strive to infer the …
SLOPE: Search with Learned Optimal Pruning-based Expansion
D Bokan, Z Ajanovic, B Lacevic - arxiv preprint arxiv:2406.04935, 2024 - arxiv.org
Heuristic search is often used for motion planning and pathfinding problems, for finding the
shortest path in a graph while also promising completeness and optimal efficiency. The …
shortest path in a graph while also promising completeness and optimal efficiency. The …
Sample complexity of learning heuristic functions for greedy-best-first and A* search
Greedy best-first search (GBFS) and A* search (A*) are popular algorithms for path-finding
on large graphs. Both use so-called heuristic functions, which estimate how close a vertex is …
on large graphs. Both use so-called heuristic functions, which estimate how close a vertex is …
Reasoning Algorithmically in Graph Neural Networks
D Numeroso - arxiv preprint arxiv:2402.13744, 2024 - arxiv.org
The development of artificial intelligence systems with advanced reasoning capabilities
represents a persistent and long-standing research question. Traditionally, the primary …
represents a persistent and long-standing research question. Traditionally, the primary …
Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies
A Pisacane, VA Darvariu, M Musolesi - arxiv preprint arxiv:2409.07932, 2024 - arxiv.org
Graph path search is a classic computer science problem that has been recently
approached with Reinforcement Learning (RL) due to its potential to outperform prior …
approached with Reinforcement Learning (RL) due to its potential to outperform prior …
Towards Optimal Planning for Green, Smart, and Semantically Enriched Cultural Tours
This concept paper presents our viewpoint regarding the exploitation of cutting-edge
technologies for the delivery of smart tourism cultural tours. Specifically, the paper reports …
technologies for the delivery of smart tourism cultural tours. Specifically, the paper reports …
Learning heuristics for A
Path finding in graphs is one of the most studied classes of problems in computer science. In
this context, search algorithms are often extended with heuristics for a more efficient search …
this context, search algorithms are often extended with heuristics for a more efficient search …