1 / 31

BUILDING A COGNITIVE SYSTEM BY GNOSYS

BUILDING A COGNITIVE SYSTEM BY GNOSYS. Co-ordinator: John Taylor (KCL)  Asst Co-Ordinator: Stathis Kasderidis (FORTH) EC PO: George Stork Start date: Oct 1; Kick-off Oct 20/21 gnosys@ics.forth.gr Web-site: http://www.ics.forth.gr/gnosys/ Department of Mathematics King’s College London, UK

eli
Download Presentation

BUILDING A COGNITIVE SYSTEM BY GNOSYS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. BUILDING A COGNITIVE SYSTEM BY GNOSYS Co-ordinator: John Taylor (KCL)  Asst Co-Ordinator: Stathis Kasderidis (FORTH) EC PO: George Stork Start date: Oct 1; Kick-off Oct 20/21 gnosys@ics.forth.gr Web-site: http://www.ics.forth.gr/gnosys/ Department of Mathematics King’s College London, UK emails: john.g.taylor@kcl.ac.uk

  2. CONTENTS • Vision of GNOSYS • GNOSYS Partners • GNOSYS Prototypes • GNOSYS Tasks/Milestones • GNOSYS Summary

  3. 1. VISION OF GNOSYS • Embodied cognition (wheel-robot + gripper) • Create concepts/rewarded-goals under attention control • Learns goal-directed tasks • Learns novel environments • Reasoning by forward models • Guidance from brain (animal/infant/adult) • Various memory types (STM/LTM/associative/error-based) • Interdisciplinary: Comp vision/ Cog NSci/ Neural Networks/ Robotics/ AI/ Maths

  4. General Work Plan of GNOSYS

  5. GNOSYS Cognitive Powers • Feature-based perception (M1-16) WP2 • Concepts/Goals/Attention (Sensory & Motor) (M6-18, 12-24) WP2/WP3 • Rewarded drive-based learning (M12-24) WP2 • Goal-based Global Computation (M6-18) WP2 • Abstraction Hierarchy (M12-24) WP3 • Reasoning/Action Planning by motor attention-base forward models(M18-33) WP3 • Robot Platforms @ 2 levels (M18/M30)

  6. HOW GNOSYS WORKS v v │ ↔ │ │ ↔ │ │ ↔ │ │ ↔ │ │ ↔ │ ANN Adaptive Stream (Concepts/ Goals/ Attention/ Rewards/ Values/ Forward Models learnt as NN predictors) Symbolic Control Threads (5 components) Linguistic Connections (Words/Fuzzy rules/ Symbolisation) : Relate to COSPAR

  7. Drives/Motivation/Rewards • Assign values (in AMYG/OBFC) as direct input (learnt), or by ‘DA’ modulation from primary rewards (satisfying basic drives) • Basic drives for GNOSYS: Energy level/ Curiosity/ Stimulation/ Minimum pain (touch/pressure)/ Approbation/ Motor activity • Use value maps --> assign value to stimuli

  8. 2. GNOSYS PARTNERS 1 King’s College London (KCL) Comp Nsci Grp: NNs, concepts, attn control • ZENON S.A., Greece (ZENON): robots 3 Foundation of Research & Technology - Hellas Greece (FORTH): global comput/robots 4 Eberhard-Karls-Universität, Tübingen, Germany (UTUB): perception/reward/robots 5 Università di Genova, Dipartimento di Informatica, Sistemistica, Telematica, Italy (UGDIST): motor control/robots -> RobotCub

  9. Attentional Agent Architect (EC FP5: DC, 2001-2003) • Distributed entity with four layers (attentional multi-level agent): • L1: Sensors • L2: Pre-processing • L3: Local decision • L4: Global decision

  10. GLOBAL CONTROL ARCHITECTURE • EXTENDED ATTENTION V EMOTION ARCHITECTURE (EC ERMIS, NF, 2002-4; BBSRC: 2004-7): (extended Corbetta & Shulman, 2002) Endogenous goals Excitatory/Inhibitory Exogenous goals Inhibitory Interaction through ACG: Excitatory Excitatory interaction Inhibition from DLPFC In emotion recognition

  11. MOTOR CORTEX ACTION NETWORK (NT, MH, OM & JGT) (in NetSim for sequence learning; tested in PDs: J NSci24:702 ) FROM OTHER CORTEX + OTHER THALAMUS MOTOR CORTEX TO OTHER CORTEX FROM CEREBELLUM STRIATUM NUCLEUS RETICULARIS THALMUS SUB-THALAMIC NUCLEUS THALAMUS CENTROMEDIAN PARAFISCULAR NUCLEUS GLOBUS PALLIDUS EXTERNAL GLOBUS PALLIDUS INTERNAL SUBSTANTIA NIGRA PARS COMPACTA SUBSTANTIA NIGRA PARS RETICULARIS GLUTAMATERGIC INPUT SIMILAR STRUCTURES MODEL OBFC, DLPFC, ACG AND VLPFC GABAERGIC INPUT DOPAMINERGIC INPUT

  12. BK DCN+ GrC DCN- PK GoC IO PONS Cerebellar Structure& Associated Regions: For Insertions,by error-based learning (with teacher) GrC granule cells GoC golgi cells BK basket cells PK purkinje cells DCN deep cerebellar nuclei (excit. & inhib.) IO inferior olive PONS pontine nuclei HIPP hippocampal regions PFC pre-frontal cortex inhibitory conns. excitatory conns. HIPP PFC

  13. HIPPOCAMPUS & AMYGDALA (in NetSim for sequence learning, and x20 speed-up in SWS) (MH, NT & JGT): as teacher

  14. EPSRC: Ventral & Dorsal Concept Learning (-> GNOSYS) Ventral pathway Dorsal pathway TE TEO V4 LIP V5 V2 V1 V1 LGN Input Learning Currently Hard-wired Hard-wired LGN Input

  15. Architecture Details: Percepts • V1: 4 excitatory & inhibitory layers for bar orientations, hardwired (14*14) • V2 (28*28) trained on reduced set of pairs of bars (6), # start positions in retina 121 • V4 (28*28)->TEO (28*28/14*14)->TE (7*7) trained on 2 different triangles (121 start positions) • Now by cluster computing • Next step: to DL/VLPFC as goals-> attention

  16. ERMIS/BBSRC: GLOBAL BRAIN CONTROL by ATTENTION: Fusiform Gyrus VCX PL PFC PL ACG/TPJ PL -> Simulated Attentional Blink NF/JGT -> Consciousness by CODAM (Prog Neurobiology 03)

  17. Model of Visuo-Motor Attention Control System (JGT + NF, IJCNN’03) -> MACS for Attention filtering ->MINDRACES for anticipation

  18. AB extended by AMYG as bias: ERPs for T2 in Lag3 when no amygdala

  19. ERPs for T2 in Lag3: amygdala input from T2’s object rep, & fed back to same site

  20. UGDIST: Biomimetic trajectory formation via artificial potential fields … the importance of smoothness and continuity … Tsuji T, Tanaka Y, Morasso P, Sanguineti V. Kaneko M (2002) IEEE Trans SMC-C, 32, 426-439. Morasso P, Sanguineti V, Spada G (1997) Neurocomputing, 15, 411-434

  21. Real-time control of robot motion by sub-symbolic neural activity The in-vitro brain Khepera: the artificial body … the importance of bidirectional communication … From the Neurobit project

  22. Robotized haptic interface … the importance of softness and a soft touch …

  23. Computational Vision and Robotics Lab (CVRL)Institute of Computer ScienceFoundation for Research andTechnology – Hellas (FORTH)

  24. Cognition Action System Architecture Perception Learning CVRL - FORTH • Mission: Study the mechanisms involved in the development of autonomous robotic systems

  25. CVRL - FORTH • Current R&D activities   • perceptual competences based on visual and range sensors and sensor fusion techniques • coupling of perception and action • autonomous navigation and control of complex robotic systems • development of networked robotic systems • content-based retrieval of images and video • Future activities • development of robotic behaviours that simulate corresponding behaviours of living organisms • emergence of cognition in artificial systems • complex heterogeneous robotic systems involving multiple robots

  26. UTUB Experienced in robot movement and planning Involved in GNOSYS perceptions & rewardsZENONRobotics Company in AthensExperienced in robot applicationsTo construct robot platforms (2)

  27. 3. GNOSYS PROTOTYPES • PROTOTYPE I (M18): Attn control of sensory inputs & response • Learn concepts of simple shapes [3] & rewarded actions, under attention • Responses to commands/learn new goals as new actions on new objects • PROTOTYPE 2 (M28): As above but more complex objects [3] + sequences of action/object pairs in real scenes + forward models for virtual goal seeking (reasoning)

  28. 4. GNOSYS TASKS, etc: Reasoning Domains/Environments (WP2&3) • Three levels of environment • Level 1: Learn shapes/colours; move & touch; move & pick up; [2] & [3]-D objects • Powers: Concept/Attn/Goals as actions on objects/Valence of objects in environment • Level 2: Complex objects & actions • Powers: ibid/manipulate to achieve goals • Level 3: Hierarchy of objects; run virtual object/action sequences to achieve goals • Powers: Reasoning/ novel objects/actions

  29. Application to Patrolling, etc • Construct loc/action and object/action map in patrol environment • Reasoning tasks: to discover actions: (loc1, action)→loc2, (obj1,action)→obj2 • Meets barrier of boxes. Reasoning: move box to pass through, instead of moving round barrier • Over pond: reasoning: find plank to put across pond • Plus many psychological tasks (WCST/Tower of London, etc, etc)

  30. MILESTONES • Level 1: Simple actions & stimuli [2] (M6) • Level 2: More complex actions & stimuli [3]/colour/motion/audition/touch (M16) • Level3: Real-world stimuli (M24) • Prototype 1 (M18) • Prototype 2 (M28) • Assessment (M34)

  31. 5. GNOSYS SUMMARY • Create concepts/goals by learning • Can handle novel environments • Embodied cognitive system • Learning by infant-style development (by hierarchy of modules sequentially coming on line) • Reasoning by forward models created by reward-based learning

More Related