Related Experiment Video
Updated: Jan 3, 2026
07:55
Generation of Escape Variants of Neutralizing Influenza Virus Monoclonal Antibodies
Published on: August 29, 2017
12.2K
A Self-Learning Immune Co-Evolutionary Network for Multiple Escaping Targets Search With Random Observable
IEEE Transactions on Neural Networks and Learning Systems
|November 15, 2019
Summary
This study introduces a novel self-learning immune co-evolutionary network (SLICEN) for the challenging multiple escaping-targets search with random observation conditions (MESROC) problem. SLICEN effectively develops advanced cooperative search behaviors without needing training data.
Area of Science:
- Robotics
- Artificial Intelligence
- Control Theory
Background:
- Cooperative control in multi-agent systems faces challenges with multiple escaping targets.
- Existing methods struggle with the assumption of continuous observations, which is often unrealistic.
Purpose of the Study:
- To address the multiple escaping-targets search with random observation conditions (MESROC) problem.
- To propose a novel self-learning immune co-evolutionary network (SLICEN) that overcomes limitations of conventional approaches.
Main Methods:
- SLICEN integrates an immune cellular network (ICN) and an immune learning algorithm (ILA).
- ICN utilizes various network models (CNNs, ELMs, SVMs) for solutions.
- ILA refines solutions through co-evolution, enabling learning without training samples.
Main Results:
- Simulations demonstrate SLICEN's ability to generate advanced cooperative search behaviors.
- The proposed method effectively solves the MESROC problem.
Conclusions:
- SLICEN offers an efficient and novel approach to complex multi-agent search problems.
- The co-evolutionary mechanism allows for adaptive behavior development without prior training data.
Related Concept Videos
01:14Avoidance Learning and Learned Helplessness
2.4K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.4K
01:22Multi-input and Multi-variable systems
353
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
In the absence of...
353
01:12Observational Learning
767
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
767
00:59Propagation of Uncertainty from Random Error
1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K
01:13Randomized Experiments
8.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
8.8K
01:09Random Variables
17.1K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.1K