HUMANOBS

HUMANOBS

Reykjavik – September 2010

Eric [email protected]

Reykjavik University - CADIA

Replicode

2

Overview

> What is Replicode?

> Main Features

> Tools

> Status & Future Work

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What is Replicode?

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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

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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 automated memory 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

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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

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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

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Main Features

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Production System

> Data-driven: patterns of time seriestnow

inputs outputs

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Production System

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Production System

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Production System

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Production System

> Time scope

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Production System

> Parallel

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Production System

> Code = data

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Production System

> Execution reflectivity

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Control

> Resilience

> Saliency

> Activation

tnow

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(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] )0us11)|[]

Code

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Code

passive data

2020



Code

passive data

program

2121



Code

patternprogram

productions

2222



Code

dynamiccodegeneration

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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

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Groups

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

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Groups

- cyclic decay of saliencies/threshold

sln thr

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

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Groups

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Groups

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Graphs

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> across the object graph

> transversal to groups

Propagation of Saliency

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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

(grab )

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(mk.val position ) (mk.val position )

(mk.val weight )

(H)

(C)

(W)

(P)

(move )

(F)(T0) (T1)

FF

FT

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Forward Chaining

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(H)

(C)

(W)

(P)

(F)(T0) (T1)

As forward models: produce predictions

world/system

inputsfromthe worldor self

measuresuccess

(grab ) (mk.val position ) (mk.val position )

(mk.val weight )

(move )

FT

FF

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Backward Chaining

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(H)

(C)

(W)

(P)

(F)(T0) (T1)

As inverse models: produce goals

world/system

inputsfromself

measuresuccess/issuecommands

(grab ) (mk.val position ) (mk.val position )

(mk.val weight )

(move )

FT

FF

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Tools

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Tools

> Compiler

> Decompiler

> Provisioning system

> Visualization system

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Tools

Visualization system

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Status & Future Work

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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

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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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