1. A neural approach to an attentive navigation for 3D intelligent virtual agents
Miguel Lozano
Institute of Robotics
Computer Science Department
University of Valencia (Spain)
Javier Molina
Neurotechnology, Control and Robotics Group
Engineering and Automatic Systems Department
Politechnics University of Cartagena (Spain)
IEEE International Conference on Systems, Man and Cybernetics. 6-9 Oct.Tunisia 2002

2. Contents Introduction Navigation requirements for 3D IVA’s
Synthetic vision, perception and Sort Time Memory (STM)
The Neural design
Results
Conclusions and Future Works
Synthetic vision, perception and Short Time Memory (STM)
IEEE International Conference on Systems, Man and Cybernetics. 6-9 Oct.Tunisia 2002

3. Introduction Computer Graphics Artificial Intelligence
Virtual Characters
Humanoids
Avatars
Non-real characters
Animals
(Fishes, Birds, ...)
The number of fully integrated sense-plan-act in a 3D VE is more reduced.
Latombe: combined 2D path-planning with Rule based system for vision/perception
Tu&Terzopoulos: Raycasting vision with physically based locomotion fot his fishes
Noder et al. Uses a synthetic vision system with object-false colouring, combining local and global techinques
The increasing graphic realism of 3D virtual character has generated
corresponding expectations on their behaviours
IEEE International Conference on Systems, Man and Cybernetics. 6-9 Oct.Tunisia 2002

4. Navigation requirements for 3D Intelligent Virtual Agents (IVA’s)
Depending on specific application ...
Reachability
Collision avoidance
Replanning (Dynamic targets)
Lifelike paths
Lifelike attention

5. Navigation requirements for 3D IVA’s
Global navigation problems ...
Reactivity
Uncertainty
(eg. Virtual Supermarket)
Realism
Discretization + Path-Smooth
.... Path-nodes in Games Engines

6. Synthetic Vision, perception and STM
IVA + sensors + memory = Exploring unknown environments
2 information channels: Interest area + Virtual Vision Sensors
In sythetic vision we can skip all the problems of distance detection, pattern recognition and noisy images
VVSensors: pyramidical culling volume from the agent point of view
IEEE International Conference on Systems, Man and Cybernetics. 6-9 Oct.Tunisia 2002

7. Synthetic Vision, perception and STM
Human vision metaphor considers vision as a complex process where input data is taken from sacadic eye movements and processed in different areas of visual and prefrontal cortex.
This will result in a finite number of categories representing the state of the environment from its point of view.

8. Synthetic Vision, perception and STM
Simulating Human cognitive information processing model using the Adaptative Resonance Theory (ART) (Carpenter&Grossberg)
Fuzzy-ART systems tries to allocate the current sensory information (input samples) in one of the familiar categories previously learned.
When agreeing with the vigilance parameter this can not be committed to any category, the system will create a new one.
In ART self organizing Neural Network this learning can be carried out on-line (the agent takes a look to the environment to categorize it)
En el último punto: No todas las redes autoorganizasdas son capaces de categorizar inputs de manera on line. Esto es una propiedad única a los sistemas ART que nos ha hecho fijarnos en este tipo de arquitectura dada la naturaleza del problema que abordamos.
IEEE International Conference on Systems, Man and Cybernetics. 6-9 Oct.Tunisia 2002

9. Synthetic Vision, perception and STM
In single ART systems a single pattern is able to activate only one category
A complete agent cycle must imply the sequential activation of different categories, the active ones
STM will manage these categories,
representing the knowledge
the agent has about its environment
(perception).
Is this useful for navigation?
... yes using the conditional paradigm ...
Vas a comentar algo mas sobre el paradigma condicional que hemos empleado?
IEEE International Conference on Systems, Man and Cybernetics. 6-9 Oct.Tunisia 2002

10. The neural design
Neuron Layers as angular velocity maps (Goal following, Correction and Motor)
Find a right assciation STM-MotorL - Clasical conditional paradigm (Using conditional learning Signals, CSn)
.... Detect collioson situation  associate a right motor response.
IEEE International Conference on Systems, Man and Cybernetics. 6-9 Oct.Tunisia 2002

11. Results
FF-Nnetwork (no training) ... Plausible problems ...

12. Results
FF-Nnetwork (no training) ... problems

13. Results
NN with on-line categorization and STM-Motor layer conditional association

14. Results
More variability at STM (vigilance parameter), the agent learns at the first loop (1 colision) ... Next time is ok !!

15. Conclusions and Future Works
NN introduced in 3D IVAs (Tu&Terzopoulos) for computer animation (Neuro-Animator).
Majority of 3D IVA’s uses global (Low cost) techinques for Navigation and attention is rarely considered (Badler)
Navigation goals introduced has been achieved (reactivity, ...)
Future integration of Attention using the same data-flow
Simply incorporating the right conditioning and a new motor layer
Next step in navigation will be to consider dynamic obstacles (other agents ...)
IEEE International Conference on Systems, Man and Cybernetics. 6-9 Oct.Tunisia 2002

16. A neural approach to an attentive navigation for 3D intelligent virtual agents
Miguel Lozano
Institute of Robotics
Computer Science Department
University of Valencia (Spain)
Javier Molina
Neurotechnology, Control and Robotics Group
Engineering and Automatic Systems Department
Politechnics University of Cartagena (Spain)