Situation Awareness through Agent Based Agent Systems Research Group, VU University Amsterdam http://asr.cs.vu.nl/

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

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

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

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

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

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

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

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

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. 459-470, Springer.

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

Thank You

Results Simulation details for Level 1 SA example.

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.