[go: up one dir, main page]

Kent et al., 2020 - Google Patents

Single-agent policies for the multi-agent persistent surveillance problem via artificial heterogeneity

Kent et al., 2020

View PDF
Document ID
18195290299793135281
Author
Kent T
Richards A
Johnson A
Publication year
Publication venue
European Conference on Multi-Agent Systems

External Links

Snippet

Modelling and planning as well as Machine Learning techniques such as Reinforcement Learning are often difficult in multi-agent problems. With increasing numbers of agents the decision space grows rapidly and is made increasingly complex through interacting agents …
Continue reading at drive.google.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/004—Artificial life, i.e. computers simulating life
    • G06N3/006—Artificial life, i.e. computers simulating life based on simulated virtual individual or collective life forms, e.g. single "avatar", social simulations, virtual worlds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation

Similar Documents

Publication Publication Date Title
Chen et al. Mobile robot path planning using ant colony algorithm and improved potential field method
Lee et al. Crowd simulation by deep reinforcement learning
Yan et al. Collision-avoiding flocking with multiple fixed-wing UAVs in obstacle-cluttered environments: A task-specific curriculum-based MADRL approach
La et al. Multirobot cooperative learning for predator avoidance
Mavrogiannis et al. Socially competent navigation planning by deep learning of multi-agent path topologies
Wang et al. Oracle-guided deep reinforcement learning for large-scale multi-UAVs flocking and navigation
Jansen et al. Direction maps for cooperative pathfinding
Kapadia et al. Parallelized egocentric fields for autonomous navigation
Nguyen et al. A deep hierarchical reinforcement learner for aerial shepherding of ground swarms
Shaoul et al. Multi-robot motion planning with diffusion models
Junior et al. Efficient strategy for collective navigation control in swarm robotics
Lima et al. A probabilistic cellular automata ant memory model for a swarm of foraging robots
Hideg et al. Uniform dispersal of robots with minimum visibility range
Junior et al. Wave algorithm applied to collective navigation of robotic swarms
Ma et al. An end-to-end deep reinforcement learning based modular task allocation framework for autonomous mobile systems
Zhao et al. Multirobot unknown environment exploration and obstacle avoidance based on a Voronoi diagram and reinforcement learning
Kent et al. Single-agent policies for the multi-agent persistent surveillance problem via artificial heterogeneity
Xi et al. A graph representation for autonomous driving
Yi et al. Indoor pursuit-evasion with hybrid hierarchical partially observable markov decision processes for multi-robot systems
Chen et al. Controlling the movement of crowds in computer graphics by using the mechanism of particle swarm optimization
Priisalu et al. Varied realistic autonomous vehicle collision scenario generation
Sardinha et al. Combining Lévy walks and flocking for cooperative surveillance using aerial swarms
Galland et al. Organizational and holonic modelling of a simulated and synthetic spatial environment
Butz et al. Inferring event-predictive goal-directed object manipulations in REPRISE
Ramírez-Bedoya et al. Implementation of reinforcement-learning algorithms in autonomous robot navigation