Carlos Hernández
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View article: Benchmarking MOEAs for solving continuous multi-objective RL problems
Benchmarking MOEAs for solving continuous multi-objective RL problems Open
Multi-objective reinforcement learning (MORL) addresses the challenge of simultaneously optimizing multiple, often conflicting, rewards, moving beyond the single-reward focus of conventional reinforcement learning (RL). This approach is es…
View article: K-Focal Search for Slow Learned Heuristics
K-Focal Search for Slow Learned Heuristics Open
Bounded suboptimal heuristic search is a family of search algorithms capable of solving hard combinatorial problems, returning suboptimal solutions within a given bound. Recent machine learning approaches have been shown to learn accurate …
View article: Heuristic-Search Approaches for the Multi-Objective Shortest-Path Problem: Progress and Research Opportunities
Heuristic-Search Approaches for the Multi-Objective Shortest-Path Problem: Progress and Research Opportunities Open
In the multi-objective shortest-path problem we are interested in computing a path, or a set of paths that simultaneously balance multiple cost functions. This problem is important for a diverse range of applications such as transporting h…
View article: Analysis and Computation of Multidimensional Linear Complexity of Periodic Arrays
Analysis and Computation of Multidimensional Linear Complexity of Periodic Arrays Open
Linear complexity is an important parameter for arrays that are used in applications related to information security. In this work we survey constructions of two and three dimensional arrays, and present new results on the multidimensional…
View article: Subset Approximation of Pareto Regions with Bi-objective A*
Subset Approximation of Pareto Regions with Bi-objective A* Open
In bi-objective search, we are given a graph in which each directed arc is associated with a pair of non-negative weights, and the objective is to find the Pareto-optimal solution set. Unfortunately, in many practical settings, this set is…
View article: Toward a Search Strategy for Anytime Search in Linear Space Using Depth-First Branch and Bound
Toward a Search Strategy for Anytime Search in Linear Space Using Depth-First Branch and Bound Open
Depth-First Branch and Bound (DFBnB) is an anytime algorithm for solving combinatorial optimization problems. In this paper we present a weighted version of DFBnB, wDFBnB, which incorporates standard techniques for using weights in heurist…
View article: A Neural Network for Decision Making in Real-Time Heuristic Search
A Neural Network for Decision Making in Real-Time Heuristic Search Open
Most real-time heuristic search algorithms solve search problems by executing a series of episodes. During each episode the algorithm decides an action for execution. Such a decision is usually made using information gathered by running a …
View article: A Suboptimality Bound for 2k Grid Path Planning
A Suboptimality Bound for 2k Grid Path Planning Open
The 2k neighborhood has been recently proposed as an alternative to optimal any-angle path planning over grids. Even though it has been observed empirically that the quality of solutions approaches the cost of an optimal any-angle path as …
View article: Compiling Cost-Optimal Multi-Agent Pathfinding to ASP
Compiling Cost-Optimal Multi-Agent Pathfinding to ASP Open
Multi-Agent Pathfinding (MAPF) over grids is the problem of finding n non-conflicting paths that lead n agents from a given initial cell to a given goal cell. Cost-optimal MAPF in addition minimizes the total number of actions performed by…
View article: Fast and Almost Optimal Any-Angle Pathfinding Using the 2k Neighborhoods
Fast and Almost Optimal Any-Angle Pathfinding Using the 2k Neighborhoods Open
Any-angle path finding on grids is an important problem with applications in autonomous robot navigation. In this paper, we show that a well-known pre-processing technique, namely subgoal graphs, originally proposed for (non any-angle) 8-c…
View article: A Learning-Based Framework for Memory-Bounded Heuristic Search: First Results
A Learning-Based Framework for Memory-Bounded Heuristic Search: First Results Open
Many existing boundedly-suboptimal heuristic search algorithms are variants of best-first search. Due to memory limitations, these algorithms are unable to solve problems with extremely large search spaces. In this paper, we present a fram…
View article: Time-Bounded Best-First Search
Time-Bounded Best-First Search Open
Time-Bounded A* (TBA*) is a single-agent deterministic search algorithm that expands states of a graph in the same order as A* does, but that unlike A* interleaves search and action execution. Although the idea underlying TBA* can be gener…
View article: Subgoal Graphs for Eight-Neighbor Gridworlds
Subgoal Graphs for Eight-Neighbor Gridworlds Open
We propose a method for preprocessing an eight-neighbor gridworld to generate a subgoal graph and a method for using this subgoal graph to find shortest paths faster than A*, by first finding high-level paths through subgoals and then shor…
View article: Paper Summary: Time-Bounded Adaptive A*
Paper Summary: Time-Bounded Adaptive A* Open
This paper summarizes our AAMAS 2012 paper on "Time-Bounded Adaptive A*," which introduces the game time model to evaluate search algorithms in real-time settings, such as video games. It then extends the existing real-time search algorith…
View article: Position Paper: Incremental Search Algorithms Considered Poorly Understood
Position Paper: Incremental Search Algorithms Considered Poorly Understood Open
Incremental search algorithms, such as D* Lite, reuse information from previous searches to speed up the current search and can thus solve sequences of similar search problems faster than Repeated A*, which performs repeated A* searches. I…
View article: Reconnecting with the Ideal Tree: An Alternative to Heuristic Learning in Real-Time Search
Reconnecting with the Ideal Tree: An Alternative to Heuristic Learning in Real-Time Search Open
In this paper, we present a conceptually simple, easy-to-implement real-time search algorithm suitable for a priori partially known environments. Instead of performing a series of searches towards the goal, like most Real-Time Heuristic Se…
View article: Real-Time Adaptive A* with Depression Avoidance
Real-Time Adaptive A* with Depression Avoidance Open
Real-time search is a well known approach to solving search problems under tight time constraints. Recently, it has been shown that LSS-LRTA∗ , a well-known real-time search algorithm, can be improved when search is actively guided away of…
View article: A Compact Answer Set Programming Encoding of Multi-Agent Pathfinding
A Compact Answer Set Programming Encoding of Multi-Agent Pathfinding Open
Multi-agent pathfinding (MAPF) is the problem of finding non-colliding paths connecting given initial positions with given goal positions on a given map. In its sum-of-costs variant, the total number of moves and wait actions performed …
View article: Solving Sum-of-Costs Multi-Agent Pathfinding with Answer-Set Programming
Solving Sum-of-Costs Multi-Agent Pathfinding with Answer-Set Programming Open
Solving a Multi-Agent Pathfinding (MAPF) problem involves finding non-conflicting paths that lead a number of agents to their goal location. In the sum-of-costs variant of MAPF, one is also required to minimize the total number of moves pe…
View article: The 2^k Neighborhoods for Grid Path Planning
The 2^k Neighborhoods for Grid Path Planning Open
Grid path planning is an important problem in AI. Its understanding has been key for the development of autonomous navigation systems. An interesting and rather surprising fact about the vast literature on this problem is that only a few n…
View article: Multipath Adaptive A*: Factors That Influence Performance in Goal-Directed Navigation in Unknown Terrain
Multipath Adaptive A*: Factors That Influence Performance in Goal-Directed Navigation in Unknown Terrain Open
Incremental heuristic search algorithms are a class of heuristic search algorithms applicable to the problem of goal-directed navigation. D* and D*Lite are among the most well-known algorithms for this problem. Recently, two …
View article: Automatic Algorithm Selection In Multi-agent Pathfinding
Automatic Algorithm Selection In Multi-agent Pathfinding Open
In a multi-agent pathfinding (MAPF) problem, agents need to navigate from their start to their goal locations without colliding into each other. There are various MAPF algorithms, including Windowed Hierarchical Cooperative A*, Flow Annota…
View article: Anytime Focal Search with Applications
Anytime Focal Search with Applications Open
Focal search (FS) is a bounded-suboptimal search (BSS) variant of A*. Like A*, it uses an open list whose states are sorted in increasing order of their f-values. Unlike A*, it also uses a focal list containing all states from the open lis…
View article: Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology
Digital processing of medical images: application in synthetic cardiac datasets using the CRISP_DM methodology Open
In this work an adaptation of the Cross Industry
\nStandard Process for Data Mining (CRISP-DM) methodology,
\nin the context of digital medical image
\nprocessing is proposed. Specifically, synthetic images reported
\nin the literature are…
View article: Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging
Low grade glioma segmentation using an automatic computational technique in magnetic resonance imaging Open
"Through this work we propose a computational technique for the segmentation of a brain tumor, identified as low grade glioma (LGG), specifically grade II astrocytoma, which is present in magnetic resonance images (MRI). This technique con…
View article: Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study
Smoothing filters in synthetic cerebral magnetic resonance images: A comparative study Open
"This paper presents the evaluation of two computational techniques for smoothing noise that might be present in synthetic images or numerical phantoms of magnetic resonance (MRI). The images that will serve as the databases (DB) during th…
View article: Volumetry of epidural hematomas in computed tomography images: Comparative study between linear and volumetric methods
Volumetry of epidural hematomas in computed tomography images: Comparative study between linear and volumetric methods Open
This work evaluates the performance of some
\nmethods employed for assessing the volume of
\nseven subdural hematomas (EDH), present in
\nmulti-layer computed tomography images. Firstly, a reference
\nvolume is considered to be that obtain…
View article: mdtraj/mdtraj: MDTraj 1.9
mdtraj/mdtraj: MDTraj 1.9 Open
[xtc] approx_nframes returns at least one (#1265) Make compute_directors user-facing (#1260) Add differentiable contacts option (#1247) Remove link to forum (#1237) Skip renumbering if no bonds in mol2 (#1238) Add a bunch of Van Der Waals …
View article: Online Bridged Pruning for Real-Time Search with Arbitrary Lookaheads
Online Bridged Pruning for Real-Time Search with Arbitrary Lookaheads Open
Real-time search algorithms are relevant to time-sensitive decision-making domains such as video games and robotics. In such settings, the agent is required to decide on each action under a constant time bound, regardless of the search spa…
View article: Grid Pathfinding on the 2<i>k</i> Neighborhoods
Grid Pathfinding on the 2<i>k</i> Neighborhoods Open
Grid pathfinding, an old AI problem, is central for the development of navigation systems for autonomous agents. A surprising fact about the vast literature on this problem is that very limited neighborhoods have been studied. Indeed, only…