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HUMANOBS

HUMANOBS. Replicode. Eric Nivel eric@ru.is Reykjavik University - CADIA. Reykjavik – September 2010. Overview. > What is Replicode? > Main Features > Tools > Status & Future Work. What is Replicode?. Replicode. > A programming language + an executive + development tools > Motivation

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HUMANOBS

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  1. HUMANOBS Replicode Eric Nivel eric@ru.is Reykjavik University - CADIA Reykjavik – September 2010

  2. Overview > What is Replicode? > Main Features > Tools > Status & Future Work

  3. What is Replicode?

  4. Replicode > A programming language + an executive + development tools > Motivation - Developmental AI machines that dynamically rewrite their own code according to their experience model-based/driven bootstrap code - Engineering for the REAL world fail gracefully and recover (what is left of) the function seed programming as opposed to paranoid programming - Open-ended dynamic environments - Working definition of intelligence: ”to adapt with incomplete knowledge and limited resources”– Wang 2006

  5. Requirements I > To synthesize and reason about concurrent processes - To dynamically produce new specifications and implementations - Tractable parallel computing stateless programs no explicit coupling/synchronization automatedmemory management - Support for forward/backward chaining – to generate predictions/goals - Support for associative memory - Support for simulation runs - Support for uncertainty – crisp <-> fuzzy - Non axiomatic programming system

  6. Requirements II > To execute programs - Soft real-time: on time, almost anytime low latency preemptibility - Execution feedback program execution success/failure of predictions/goals resource usage both quantitative and qualitative - Scalability wrt CPU cores and RAM - Distribution over machine clusters

  7. In a Nutshell > Production system > Soft real-time > Data-driven - external and internal inputs > Parallel - computation performed breadth-first > Pattern matching at arbitrary scales – time/structural depth, span > Arbitrary granularity - low, numerous, crisp <-> high, sparse, fuzzy > Dynamic – {add, delete, control} {code/data/ontology} > Execution feedback > Distributed > Scalable

  8. Main Features

  9. Production System > Data-driven: patterns of time series now t inputs outputs

  10. Production System

  11. Production System

  12. Production System

  13. Production System > Time scope

  14. Production System > Parallel

  15. Production System > Code = data

  16. Production System > Execution reflectivity

  17. Control > Resilience > Saliency > Activation now t

  18. Code (mk.val self position (vec3 1 1 1) 1) [] [now 1 forever root nil] _start:(pgm |[] [] [] (ptn (mk.val self position v1#vec3: ::) |[]) |[] |[] [] (inj [] p:(pgm |[] [] [] (ptn (mk.val e: position v2:(vec3 x: y: z:) ::) [(\ (= e self))]) |[] |[] [] (inj [] (mk.val self position (vec3 v1.y 10 (\ (+ z 1))) 1) [(\ now) 1 forever root nil] ) 0us 1 1 ) [now 0 forever root nil] ) (inj [] (ins p |[]) [now 0 forever root nil 1] ) 0us 1 1 ) |[] 18

  19. Code (mk.val self position (vec3 1 1 1) 1) [] [now 1 forever root nil] _start:(pgm |[] [] [] (ptn (mk.val self position v1#vec3: ::) |[]) |[] |[] [] (inj [] p:(pgm |[] [] [] (ptn (mk.val e: position v2:(vec3 x: y: z:) ::) [(\ (= e self))]) |[] |[] [] (inj [] (mk.val self position (vec3 v1.y 10 (\ (+ z 1))) 1) [(\ now) 1 forever root nil] ) 0us 1 1 ) [now 0 forever root nil] ) (inj [] (ins p |[]) [now 0 forever root nil 1] ) 0us 1 1 ) |[] passive data 19

  20. Code (mk.val self position (vec3 1 1 1) 1) [] [now 1 forever root nil] _start:(pgm |[] [] [] (ptn (mk.val self position v1#vec3: ::) |[]) |[] |[] [] (inj [] p:(pgm |[] [] [] (ptn (mk.val e: position v2:(vec3 x: y: z:) ::) [(\ (= e self))]) |[] |[] [] (inj [] (mk.val self position (vec3 v1.y 10 (\ (+ z 1))) 1) [(\ now) 1 forever root nil] ) 0us 1 1 ) [now 0 forever root nil] ) (inj [] (ins p |[]) [now 0 forever root nil 1] ) 0us 1 1 ) |[] passive data program 20

  21. Code (mk.val self position (vec3 1 1 1) 1) [] [now 1 forever root nil] _start:(pgm |[] [] [] (ptn (mk.val self position v1#vec3: ::) |[]) |[] |[] [] (inj [] p:(pgm |[] [] [] (ptn (mk.val e: position v2:(vec3 x: y: z:) ::) [(\ (= e self))]) |[] |[] [] (inj [] (mk.val self position (vec3 v1.y 10 (\ (+ z 1))) 1) [(\ now) 1 forever root nil] ) 0us 1 1 ) [now 0 forever root nil] ) (inj [] (ins p |[]) [now 0 forever root nil 1] ) 0us 1 1 ) |[] program pattern productions 21

  22. Code (mk.val self position (vec3 1 1 1) 1) [] [now 1 forever root nil] _start:(pgm |[] [] [] (ptn (mk.val self position v1#vec3: ::) |[]) |[] |[] [] (inj [] p:(pgm |[] [] [] (ptn (mk.val e: position v2:(vec3 x: y: z:) ::) [(\ (= e self))]) |[] |[] [] (inj [] (mk.val self position (vec3 v1.y 10 (\ (+ z 1))) 1) [(\ now) 1 forever root nil] ) 0us 1 1 ) [now 0 forever root nil] ) (inj [] (ins p |[]) [now 0 forever root nil 1] ) 0us 1 1 ) |[] dynamic code generation 22

  23. Reduction Modes > pgm/A reduces whenever there exists an A > pgm/|A reduces whenever there exists a B != A > |pgm/A reduces whenever there has been no A during the last TSC, or nothing >|pgm/|A reduces whenever there has been no |A, (i.e. only As) during the last TSC, or nothing > pgm/0 reduces every TSC microseconds

  24. Groups - saliency/activation thresholds - collective agreement on control values - update period

  25. Groups - saliency/activation thresholds - collective agreement on control values - update period - cyclic decay of saliencies/threshold sln thr

  26. Groups

  27. Groups

  28. Graphs

  29. Propagation of Saliency > across the object graph > transversal to groups

  30. High-level Reasoning > 1st order predicates / invertible functions / fuzzy values > Reductions at the scale of groups graphs  programs groups  models hierarchies of models dynamic model revision (T0) FT (T1) (F) FF (P) (W) (grab ) (move ) (mk.val position ) (mk.val position ) (mk.val weight ) (H) 30 (C)

  31. Forward Chaining As forward models: produce predictions world/system measure success (T0) FT (T1) (F) FF inputs from the world or self (P) (W) (grab ) (move ) (mk.val position ) (mk.val position ) (mk.val weight ) (H) 31 (C)

  32. Backward Chaining As inverse models: produce goals world/system measure success / issue commands inputs from self (T0) FT (T1) (F) FF (P) (W) (grab ) (move ) (mk.val position ) (mk.val position ) (mk.val weight ) (H) 32 (C)

  33. Tools

  34. Tools > Compiler > Decompiler > Provisioning system > Visualization system

  35. Tools Visualization system

  36. Status & Future Work

  37. Status > V1.0: fully implemented performance according to requirements (latency, scalability) > Next release: 30 Oct 2010 V1.1 = V1.0 + high-level reasoning > Distribution using mBrane joint work with CMLabs > Tools are minimal but sufficient for now

  38. Future Work > Under works: causality/context discovery using Long Short-Term Memory neural networks (Schmidhuber) neural nets programs > Under investigation: formal verification joint work with RU/ICE-ROSE (M. Sirjani) ~

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