1. Situation Awareness through Agent Based
Agent Systems Research Group,
VU University Amsterdam

2. Situation Awareness
Poor SA has been noted as a main cause behind in many aviation related tragic events (Endsley & Garland, 2000; Endsley, 1999)
SA describes the subjective quality of awareness of a situation a person is engaged in
The construct of SA is a nontrivial challenge mainly because of
poor understanding in the scientific area of human cognition and
the associated complexity in aviation domain

3. Research Questions
Does Endsley’s SA model comply with current neuro cognitive evidences?
How to design a computational model for SA adhering to the latest neuro cognitive research evidences to capture cognitive basis behind:
failure to correctly perceive information
failure to comprehend the situation
failure to project situation into the future
conflict between what is predicted and what actually occurs

4. SA by Endsley She defined SA as
"the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future“ (Endsley, 1995, pp. 36)
Endsley, 2011 pp. 11

5. How ABM used for above questions
A cognitive model is developed that can be used to express SA related process as an agent
This model provides insights for poor SA cases from cognitive perspective
This contributes to micro-level information on action selection

6. Specifics about the ABM
The developed model includes aspects of both symbolic and connectionist systems
Dynamical systems perspective is used to translate the model into a computer program

7. Pros of ABM
Agent technology is a new paradigm to solve complex system with emergent properties
Provides possibility to assess causality
Natural description provided for the workings of a situation/scenario
Contributes to provide more realistic behaviors for complex simulations
ABM simulates individuals (supports bottom-up) and can be included intangible concepts (e.g., cognitive states)
Helps to form new hypotheses
Supports to combine heterogeneous(/interdisiplinary) domains/areas

8. Cons of ABM
Realistic results highly related with quality of agent models
Specially cognitive models are difficult to design and develop
Most of the models used are too simple and not generic (need new models in different contexts)
Relation on behaviour on micro-level to behaviour of the overall system is very challenging in complex applications
Missing micro-macro link
Parameter estimation is a non-trivial task

9. Results
The designed model was validated with 4 examples collected from aviation domain:
For Level 1 SA: ‘Focusing on recapturing the LOC and not monitoring the G/S’
For Level 2 SA: ‘Applying a fuel imbalance procedure without realizing it is an engine fuel leak’
For Level 3 SA: ‘Expecting an approach on a particular runway after having received ATIS information and being surprised to be vectored for another runway’
Conflict between what is predicted and occurred

10. Results
Thilakarathne, D.J. (to appear). Modelling of Situation Awareness with Perception, Attention, and Prior and Retrospective Awareness. Biologically Inspired Cognitive Architectures, Elsevier.
Thilakarathne, D.J. (to appear). A Neurologically Inspired Model of the Dynamics of Situation Awareness under Biased Perception. In: Proceedings of the 28th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, IEAAIE'15. LNCS.
Thilakarathne, D.J. (2014). Neurologically Inspired Computational Cognitive Modeling of Situation Awareness. In: Proceedings of the International Conference on Brain Informatics and Health, BIH'14: Warsaw, Poland. LNCS, 8609, pp , Springer.

11. Summary
This type of cognitively and neurologically inspired computational models provide new directions
to develop systems that are more aligning with realistic human mental processes in aviation simulations
interpreting SA more aligning with latest neurogognitive evidences
Learning, formal model specification language, cognitive enabled training, collaborative decision making are future extensions

12. Thank You

13. Results Simulation details for Level 1 SA example.

14. Simulation details for Level 3 SA example.
Results
Simulation details for mismatch between the predicted and actual effects of an action.
Simulation details for Level 3 SA example.