Papers Problem Decomposition
“Problem Decomposition” 태그가 달린 논문 54편 · 필터 해제
A Surrogate-Assisted Variable Grouping Algorithm for General Large Scale Global Optimization Problems
Problem decomposition plays a vital role when applying cooperative coevolution (CC) to large scale global optimization problems. However, most learning-based decomposition algorithms either only apply to additively separ…
global-optimizationProblem DecompositionFast reinforcement learning with generalized policy updates
The combination of reinforcement learning with deep learning is a promising approach to tackle important sequential decision-making problems that are currently intractable. One obstacle to overcome is the amount of data …
Decision MakingProblem Decompositionreinforcement-learningReinforcement Learning+2A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems
Autonomous cyber-physical systems (CPS) can improve safety and efficiency for safety-critical applications, but require rigorous testing before deployment. The complexity of these systems often precludes the use of forma…
Autonomous VehiclesCollision AvoidanceProblem DecompositionSurveyDivide-and-Conquer Large Scale Capacitated Arc Routing Problems with Route Cutting Off Decomposition
The capacitated arc routing problem is a very important problem with many practical applications. This paper focuses on the large scale capacitated arc routing problem. Traditional solution optimization approaches usuall…
ARCProblem DecompositionLearning Reward Machines for Partially Observable Reinforcement Learning
Reward Machines (RMs), originally proposed for specifying problems in Reinforcement Learning (RL), provide a structured, automata-based representation of a reward function that allows an agent to decompose problems into …
Partially Observable Reinforcement LearningProblem Decompositionreinforcement-learningReinforcement Learning+1Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented with Fuzzy Clustering
Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services. Centralized solutions for coordinating multi-robo…
ClusteringDisaster ResponseGraph MatchingProblem Decomposition+2Decomposition Methods with Deep Corrections for Reinforcement Learning
Decomposition methods have been proposed to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate t…
Autonomous DrivingDecision MakingManagementProblem Decomposition+5Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks
Deep reinforcement learning yields great results for a large array of problems, but models are generally retrained anew for each new problem to be solved. Prior learning and knowledge are difficult to incorporate when tr…
Deep Reinforcement LearningMuJoCoProblem Decompositionreinforcement-learning+2Meta-Optimizing Semantic Evolutionary Search
I present MOSES (meta-optimizing semantic evolutionary search), a new probabilistic modeling (estimation of distribution) approach to program evolution. Distributions are not estimated over the entire space of programs. …
Problem DecompositionRecursive Decomposition for Nonconvex Optimization
Continuous optimization is an important problem in many areas of AI, including vision, robotics, probabilistic inference, and machine learning. Unfortunately, most real-world optimization problems are nonconvex, causing …
Combinatorial Optimizationgraph partitioningProblem DecompositionProtein FoldingWhittle Index Policy for Crawling Ephemeral Content
We consider a task of scheduling a crawler to retrieve content from several sites with ephemeral content. A user typically loses interest in ephemeral content, like news or posts at social network groups, after several d…
Problem DecompositionSchedulingMAPP: a Scalable Multi-Agent Path Planning Algorithm with Tractability and Completeness Guarantees
Multi-agent path planning is a challenging problem with numerous real-life applications. Running a centralized search such as A* in the combined state space of all units is complete and cost-optimal, but scales poorly, a…
AllProblem DecompositionCollective Mind: cleaning up the research and experimentation mess in computer engineering using crowdsourcing, big data and machine learning
Software and hardware co-design and optimization of HPC systems has become intolerably complex, ad-hoc, time consuming and error prone due to enormous number of available design and optimization choices, complex interact…
Problem DecompositionDeciding Monotone Duality and Identifying Frequent Itemsets in Quadratic Logspace
The monotone duality problem is defined as follows: Given two monotone formulas f and g in iredundant DNF, decide whether f and g are dual. This problem is the same as duality testing for hypergraphs, that is, checking w…
Problem Decomposition