Cognitive Architecture & Virtual Humans 2018 (47x)


This text discusses various aspects of virtual humans, including their use in military training, simulations and intelligent tutoring, as well as their cognitive abilities and embodied aspects of behavior. It also mentions the use of cognitive architectures and pedestrian dynamics in virtual human technology, and the importance of naturalistic interactions and user modeling in virtual human design. The text also touches on topics such as dyslexia, visualization, and the processing demands on cognitive architectures in virtual humans.

Cognitive architectures in virtual humans are responsible for simulating the cognitive abilities and processes of human beings, including perception, learning, memory, decision-making, and problem-solving. These architectures must be able to process and analyze a wide range of data inputs, including sensory information, past experiences, and cultural and social context, in order to generate appropriate responses and behaviors.

One key challenge in designing cognitive architectures for virtual humans is to balance the processing demands on the system with the need for real-time performance. Virtual humans must be able to process and analyze data inputs quickly and accurately in order to generate appropriate responses in a timely manner. This requires the use of efficient algorithms and data structures, as well as the use of parallel processing and other techniques to optimize performance.

Another challenge is to design cognitive architectures that are flexible and adaptable enough to handle a wide range of tasks and environments. Virtual humans may be used in a variety of different contexts and may need to be able to adapt to new tasks or situations quickly and effectively. This requires the use of modular, hierarchical, or other flexible architectures that can be easily modified or extended to meet new requirements.

Finally, cognitive architectures for virtual humans must be able to handle the complexity and uncertainty of real-world environments. This may require the use of probabilistic or Bayesian approaches, as well as the integration of machine learning and other adaptive techniques to enable the system to learn from its experiences and improve its performance over time.

Affective systems in virtual humans refer to the representation and simulation of emotions in artificial agents. These systems can be used to give virtual humans the ability to express emotions and respond to emotions in a more natural and realistic way. They can also be used to model how emotions influence cognitive processes and behaviors.

There are different approaches to designing affective systems for virtual humans. One approach is based on appraisal theory, which suggests that emotions are triggered by the evaluation of events or stimuli in relation to an individual’s goals, beliefs, and needs. This approach involves creating models that can simulate how virtual humans evaluate events and how this evaluation leads to the experience of different emotions.

Another approach involves using machine learning techniques to learn from data about how humans express and respond to emotions. This can involve using large datasets of human emotional behavior to train a model to recognize and generate emotional expressions and behaviors.

There are also hybrid approaches that combine elements of both appraisal theory and machine learning. These approaches aim to create more realistic and believable virtual humans by integrating knowledge about the psychological and physiological underpinnings of emotions with data-driven machine learning techniques.

Cognitive architectures, also known as behavior selection systems, are computational models that aim to simulate and understand the processes involved in human cognition. They are used to develop intelligent agents, including virtual humans, that can perform tasks such as natural language processing, decision-making, and problem-solving.

Cognitive architectures are composed of a set of modules or components that work together to process and represent information, execute actions, and adapt to changing environments. These modules can include perception, attention, memory, reasoning, and decision-making, among others.

Behavior selection systems, a subcategory of cognitive architectures, are designed to select and execute behaviors based on the current state of the environment and the goals of the agent. They often include a decision-making component that weighs the potential outcomes of different behaviors and chooses the one that is most likely to achieve the agent’s goals.

In the context of virtual humans, cognitive architectures and behavior selection systems play a critical role in enabling the virtual human to respond appropriately to different situations and interact naturally with humans. They allow the virtual human to perceive its environment, remember and use past experiences, make decisions, and execute actions based on its goals and the current context.



See also:

100 Best Cognitive Architecture VideosCognitive Architecture & Dialog Systems 2018Cognitive Architecture & RoboticsCognitive Architecture Meta Guide

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