ComplexWorld PhD Project: Modeling Interlevel Relations within ATM Nataliya M. Mogles VU University Amsterdam, The Netherlands

Overview Background Proposed Approach Conclusions 2

ComplexWorld PhD Projects Sponsored by ComplexWorld Network (One of the SESAR WP-E Networks) Coordinated by the Innaxis Foundation

Background SESAR Programme envisions: ‘an affordable, seamless European ATM system, enabling all categories of airspace users to conduct their operation with minimum restrictions and maximum flexibility’ Complete Process ‘from early planning through flight execution to post flight activities’ Complex System (of human/automated agents) Optimal Performance with Minimal Chance for Hazards

Example Scenario 5

“During taxiing an aircraft moves from one sector of the airport to another, until it reaches the runway designated for takeoff. The crew of the aircraft consists of the pilot-in-command and the second pilot. The monitoring and control over the traffic in each sector is performed by a dedicated ground controller. Furthermore, the control over the aircraft on the runway and in its surroundings is performed by a dedicated runway controller. These controllers are situated in different towers at the airdrome, each of which is guided by a Tower Controllers Supervisor. During taxiing, control over the aircraft is handed over from one controller to another, depending on the physical position of the aircraft. Before crossing the runway on its way, the crew of the taxiing aircraft requests the controller responsible for the runway for clearance. When the clearance is provided, the aircraft is allowed to cross.”

Processes at Different Levels Global Level: Successful and Safe Taxiing of the Aircraft from Sector A to B Local Level: Decision Making Processes of Individuals Communication between Individuals Interpretation of Communicated Information Effects of Emotions, Stress and Workload

Typical Questions How can descriptions at a global level of the system be related to descriptions at local levels and the organization of interactions? How does a change in the behavior of a local component or of the interaction organization impact the behavior of the global system? Can descriptions be found of the behavior at the global level that approximate the behavior of the local elements combined, but abstract from the local details?

Typical Questions How can descriptions at a global level of the system be related to descriptions at local levels and the organization of interactions? How does a change in the behavior of a local component or of the interaction organization impact the behavior of the global system? Can descriptions be found of the behavior at the global level that approximate the behavior of the local elements combined, but abstract from the local details? Proposed Solution: Interlevel Relations

3D Classification Scheme

Descriptions at the Behavioural Level direct reactive behaviour (relating received input to an immediate response) Descriptions at the Cognitive Level cognitive states (e.g., desires, beliefs, levels of trust, …) affective states (e.g., emotions, stress, …) learning states Descriptions at the Physiological Level activation states of neurons and connections between neurons levels of adrenalin or blood sugar Process Abstraction Dimension

Descriptions at the Temporally Local Level relating states of a process over small time steps basic mechanisms often used for simulation Descriptions at the Temporally Global Level descriptions of a process over longer time periods emerging patterns often used as requirements Temporal Dimension

Descriptions at the Individual Agent Level describing characteristics of each agent separately often considered more realistic Descriptions at the Agent Cluster Level describing multiple agents with the same characteristic as a single entity or cluster using sub-populations lower computational complexity Agent Cluster Dimension

3D Classification Scheme

Interlevel relations for process abstraction levels describe how a more abstract (global) process description can be related to less abstract (local) descriptions. Interlevel relations for temporal levels describe how a temporally more global description can be related to temporally more local descriptions. Interlevel relations for agent cluster levels describe how descriptions of a cluster relate to descriptions of its elements. Interlevel Relations

Techniques: quantitative methods (e.g., mathematical analysis to establish numerical properties) qualitative methods (e.g., logical analysis to prove entailment relations) Goal: Gain more insight into desired properties at local levels to ensure desired behavior at a global level: Which characteristics are required for (human and software) agents in ATM processes, to ensure optimal performance with minimal errors? Interlevel Relations - Methodology

Interlevel relations Within the area of Agent-Based Modeling, many different types of models exist Cognitive Models Emotion Models Social Models Neurologically Inspired Models … More structure is useful for classification 17

Relating Models at Different Levels Example: Two models at different points within one dimension may refer to the same process Question: can we define an explicit relation between these models? Unified specification format for such relations 18

Elements in Interlevel Relations 1. An ontology mapping  to relate basic state properties 2. A dynamic property mapping  * extending the basic ontology mapping to dynamic properties 3. Logical relations between dynamic properties 19

Example a 2 it is cold and dry (e.g., colddry) b 2 it is warm(e.g., warm) a 1 the molecules have a certain low level of movement and not many water molecules are present (e.g., lowmovenowater) b 1 the molecules have a certain high level of movement (e.g., highmove)  (a 2 ) = a 1  (b 2 ) = b 1 20 Elements in Interlevel Relations

21 a2a2 b2b2 a1a1 b1b1  higher level ontology lower level ontology Elements in Interlevel Relations

22 Extended mapping for dynamic properties To define interlevel relations from dynamic properties of the higher level model to dynamic properties of the lower level Elements in Interlevel Relations

23  (b 2 ) (a2) (a2) b2b2 a 2 ** higher level lower level  *( a 2  b 2 ) =  *( a 2 )  *( b 2 ) Elements in Interlevel Relations

Logical Relations between dynamic properties Relationships based on valid logical implications that indicate how mapped higher level dynamic properties can be related to dynamic properties from the lower level model. 24 Elements in Interlevel Relations

25 a 1  b 1 c 1  d 1 b 1  c 1 a 1  d 1 a 1  b 1 & b 1  c 1 & c 1  d 1  a 1  d 1 Elements in Interlevel Relations

Conclusion Advantages within analysis and design of complex systems: Conceptual clarification Complexity management Communication with non-experts Top-down design approaches Insight in weaknesses and bottlenecks in organization Increasing resilience of a complex system 26

Thank you! 27