PAM Talk AISB-11

8/7/2019 PAM Talk AISB-11

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Piagetian Autonomous Modeller

( PAM )

Michael Miller

April 5, 2011

Copyright Michael S. P. Miller 2011. All rights reserved.


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Overview

1. Research Goals2. PAM

3. Monads

4. Schemata Behavioral Equilibration

Structural Inference

1. Schematics Decomposition Use Cases Components

Data Flow Experiments

1. Implementation Status

2. Conclusions


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Research Goals

1. Replicate Sensorimotor and Pre-operational phases

2. To create smarter artificial systems that Can model the environment

Exhibit developmental stages

Reliably Predict transformations in the environment

Learn from failure

Perform multi-strategy inference

1. Test whether or not monads and schemata can model

an environment

2. Unify the work of Gary Drescher and Ryszard Michalski


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PAM - Whats Different from other systems?

1. Monads

2. How activation is spread

3. Two kinds of schemata:

Structural Behavioral

1. Using multi-strategy inference to extend the model

2. Consolidation

Automaticity Forgetting

1. Behavior equilibration with genetic operations


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PAM - Assumptions

1. Humans construct mental representations of

a. the structure of their environment

b. the transformations within their environment

2. Monads and schemata suffice for building a model

3. PAM is domain agnostic (all domain specific percept

and effect assertions are mapped to a domain

independent representation, viz. monads)


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PAM - Constraints

1. PAM must run on existing computing technology No specialized hardware required

1. Non-functional constraints: Real time performance

Resilient (i.e., fault-tolerant)

Available

Scalable


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PAM - System Context


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PAM Phase 1


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PAM - Phase 2

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PAM Target System - Phase 2


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Monads


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Monads Representation


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Monads Attributes

Identifier


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Monads Regions


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Monads Tiers

Reificat io

n


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Monads Activation


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Monads - Detectors and Effectors

Detectors Assert Percepts

Effectors Perform Commands Assert Effects (i.e. command status)


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Monads - Detectors and Effectors


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Schemata


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Schemata - Behavioral

Enablers Enables

ImpedesImpeders

Behavior := (C P, s)

where

C: context

P: prediction

s: time span


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Schemata Behavioral (within region)


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Equilibration Marginal Attribution

A failed behavior A is refined to identify a failure cause B.

A. A.

B.


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Equilibration Crossover

Successful behaviors x and y are crossed to produce z.


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Equilibration Mutation

Successful behavior x is mutated to create A new behavior y

by randomly deleting enablera and inserting enablerk.


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Consolidation

Forget

Remove low salience, redundant, or unreachable schemata

(garbage collection)

Automate

Combine high salience behaviors by eliminating intermediate

structures: e.g. A B C D is fused into A D


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Schemata Structural - Cases and Events


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Schemata Structural - Cases and Events


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Schemata Structural - Types and Plans


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Inference Simple Analogy


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Inference Simple Concretion


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Inference Simple Deduction


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Inference

Inference is performed by adding structural schemata tothe model according to Michalskis Inferential Theory.

* Reproduced from Michalski


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Schematics Decomposition


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Schematics Use Cases


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Schematics Components


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Schematics Data Flow


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Experiments Proposed

Foraging Domain (Chaput) Pioneer 3DX robot simulation

Robot Play Domain (Kaplan) Wireless mobile robot w/ Audio Visual sensors


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Implementation Status

Currently in Detailed Design

Performing alternative analysis for

Agent Platform (High performance / FIPA compliant)

Database (SQL / RAM SQL / No-SQL)

Open Issues

Scalable Join Matching Algorithm

Action Selection Algorithm

Incremental Type / Plan Induction Algorithm


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Conclusions

1. Activation defined as recency

2. Two kinds of schemata: Structural Behavioral

1. Using multi-strategy inference to extend themodel

2. Consolidation Automaticity Forgetting

1. Behavior equilibration with genetic operations

Should be fun !!


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Images

Image of the Pythagorean Monad (slide 10). Hemenway, Priya. Divine Proportion: PhiIn Art, Nature, and Science. Sterling Publishing Company Inc., 2005, p. 56.

ISBN 1-4027-3522-7

Image of Inference methods (slide 33) from Tecuci , Gheorghe & Michalski, Ryszard S.Inferential Theory of Learning. Machine Learning, A Multistrategy Approach,

Volume IV (1993) Reproduced.

Slides adapted from Michalski et. al

Image of System Context (slide 9), adapted from Hausser, Roland. A ComputationalModel of Natural Language Communication: Interpretation, Inference and Production in

Database Semantics (2010)

All other images are original.


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Questions?

http://piagetmodeler.tumblr.com

[email protected]

PAM Talk AISB-11

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