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Building Anticipatory Immune Systems for Behavioral Control in Robotics

Explore the development and implementation of Artificial Immune Systems to anticipate and control robotic behaviors. Delve into the application of Immune Systems in specific scenarios, including cognitive functions and defense mechanisms against foreign organisms. Witness the growth of robotic agents in interactive settings, inspired by the interactivism concept. Discover the intricate layers of AIS, from Antigens to T-Cells, highlighting the adaptive processes and network dynamics. Uncover how AIS perceive and respond to environmental stimuli, enhancing robot learning and predictive capabilities.

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Building Anticipatory Immune Systems for Behavioral Control in Robotics

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  1. 1st Review Meeting An Anticipatory Immune System for Behavioural Control MindRACES, First Review Meeting, Lund, 11/01/2006

  2. Overview • Activities in specific scenarios • OFAI’s robot hardware and test bed • Artificial Immune Systems for Anticipation MindRACES, First Review Meeting, Lund, 11/01/2006

  3. OFAI scenario • A “hound” growing up (Scenario 2 and bridge to Scenario 3) • Inspired by the idea of interactivism • Suited to implement and evaluate the Artificial Immune System Architecture MindRACES, First Review Meeting, Lund, 11/01/2006

  4. In the beginning ... ISTC-CNR watchdog scenario MindRACES, First Review Meeting, Lund, 11/01/2006

  5. Scenario 2: The game room scenario • Three developmental stages: • Capturing basic „how to” knowledge • Generalisation • Hunter-Prey – Bridge to Scenario 3 MindRACES, First Review Meeting, Lund, 11/01/2006

  6. First developmental stage:Capturing basic “how to” knowledge MindRACES, First Review Meeting, Lund, 11/01/2006

  7. First developmental stage:Capturing basic “how to” knowledge Gibson 1966: „Our perception of the world is expressed in terms of our interactions with it“ MindRACES, First Review Meeting, Lund, 11/01/2006

  8. Second developmental stage:Generalisation MindRACES, First Review Meeting, Lund, 11/01/2006

  9. Second developmental stage:Generalisation MindRACES, First Review Meeting, Lund, 11/01/2006

  10. Second developmental stage:Generalisation MindRACES, First Review Meeting, Lund, 11/01/2006

  11. Second developmental stage:Generalisation MindRACES, First Review Meeting, Lund, 11/01/2006

  12. Second developmental stage:Generalisation MindRACES, First Review Meeting, Lund, 11/01/2006

  13. Second developmental stage:Generalisation MindRACES, First Review Meeting, Lund, 11/01/2006

  14. Second developmental stage:Generalisation MindRACES, First Review Meeting, Lund, 11/01/2006

  15. Third developmental stage:Hunter-Prey – bridge to Scenario 3 MindRACES, First Review Meeting, Lund, 11/01/2006

  16. Third developmental stage:Hunter-Prey – bridge to Scenario 3 MindRACES, First Review Meeting, Lund, 11/01/2006

  17. Third developmental stage:Hunter-Prey – bridge to Scenario 3 MindRACES, First Review Meeting, Lund, 11/01/2006

  18. The robots MindRACES, First Review Meeting, Lund, 11/01/2006

  19. The robots – AIBO MindRACES, First Review Meeting, Lund, 11/01/2006

  20. The robots – KURT 3D • KURT 3D • Designed as “Sewage inspection robot” • Max. speed 4.0 m/s • Laser scanner • Stereo vision • Short range sensors (IR, US) • Actuator MindRACES, First Review Meeting, Lund, 11/01/2006

  21. The test bed • Fence-like barrier • Adjusted to the ecological niche • Complexity can be increased • Objects can be added MindRACES, First Review Meeting, Lund, 11/01/2006

  22. The test bed – Scenario 2 MindRACES, First Review Meeting, Lund, 11/01/2006

  23. The test bed – Scenario 3 MindRACES, First Review Meeting, Lund, 11/01/2006

  24. The test bed – Objects MindRACES, First Review Meeting, Lund, 11/01/2006

  25. The test bed – Objects MindRACES, First Review Meeting, Lund, 11/01/2006

  26. Artificial Immune Systems for Anticipation • Introducing Immune Systems • Artificial Immune Systems • Growing up a robot in the game room scenario • AIS for Anticipation – AISA MindRACES, First Review Meeting, Lund, 11/01/2006

  27. Introducing Immune Systems • “A complex system of cellular and molecular components having the primary function of distinguishing selffrom not self and defense against foreign organisms or substances” (source: Dorland's Illustrated Medical Dictionary, online) • “The immune system is a cognitive system whose primary role is to provide body maintenance” (Iren Cohen) MindRACES, First Review Meeting, Lund, 11/01/2006

  28. What are Artificial Immune Systems? • “AIS are computational systems inspired by theoretical immunology and observed immune functions, principles and models, which are applied to complex problem domains.” (Jon Timmis, University of Kent at Canterbury, 2003) MindRACES, First Review Meeting, Lund, 11/01/2006

  29. Growing up a robot in the game room scenario Basic Motivations Learns behaviours and anticipates AISA MindRACES, First Review Meeting, Lund, 11/01/2006

  30. Multiple Antigens for each situation Antigen represents thecomplete data universe T-Cells Artificial Immune Systemsfor Anticipation – AISA Immune Layer Clonal selection, Mutation, Network dynamics, Negative and positive selection Affinity Layer Euclidean, Hamming, Manhattan – Similar, Complementary Representation Layer Sensor and Data – Memory Pool / Data Universe Sensor input data from robotic agents  Data processing  Virtual Sensors  Output to processing nodes (AIS, etc.) MindRACES, First Review Meeting, Lund, 11/01/2006

  31. Representing the environment:Antigensand antibodies Immune System AIS Data pool Raw and filtereddata (series) fromreal and virtual sensors Antigen (Virus, Bacteria, etc.) MindRACES, First Review Meeting, Lund, 11/01/2006

  32. Representing the environment:Antigensand antibodies Immune System AIS Action C A E Antibody (Lymphocyte) Condition Expectation MindRACES, First Review Meeting, Lund, 11/01/2006

  33. Capturing basic “how to” knowledge • Network generation Antigen MindRACES, First Review Meeting, Lund, 11/01/2006

  34. Capturing basic “how to” knowledge • Network generation C A E Antigen MindRACES, First Review Meeting, Lund, 11/01/2006

  35. Capturing basic “how to” knowledge • Network generation C A E Antigen MindRACES, First Review Meeting, Lund, 11/01/2006

  36. Multilayer Abstraction Emotion Module Network2 Generalisation(based on lower level) Network1 Basic Capabilities („How-to“ Knowledge) DRIVES Data pool Boredom,Curiosity Raw and filtereddata (series) fromreal and virtual sensors MindRACES, First Review Meeting, Lund, 11/01/2006

  37. Multilayer Abstraction „AIBO, fetch the ball“ Emotion Module Network2 Generalisation(based on Network1) Approach Ball C A E Ball straight ahead Ball closer MindRACES, First Review Meeting, Lund, 11/01/2006

  38. Emotion and Surprise Module Filter A Sensor 1 Filter C AIS Control Architecture Sensor 2 Robotic Agent (Raw Sensor Data) Filter B Sensor 3 Integration of Mechanisms Introduction of emotions andcomplex drives to the AIS ISTC-CNR, IST Any sensory filter (especially vision) modules andmechanisms,e.g. FoveaMechanismIDSIA, LUCS, Any partner Comparison UW, ISTC-CNR AIBO Simulator and Real Robot –Experimentation, Experiences and NBU, IST Programmes MindRACES, First Review Meeting, Lund, 11/01/2006

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