Situation decomposition method extracts partial data which contains some rules.

Hiroshi Yamakawa

(FUJITSU LABORATORIES LTD.)

Abstract In an infantile development process, the fundamental knowledge about the external world is

acquired through learning without clear purposes. An adult is considered to use that fundamental knowledge for various works. The acquisition of the internal model in these early stages may exist as a background of the flexible high order function of human brain. However, research of such learning technology is not progressing to nowadays.

The system can improves prediction ability and reusability in the lasting work by using the result of learning without clear purposes. Then, we proposed the situation decomposition technology which chooses the partial information which emphasizes the relation "another attribute value will also change if one attribute value changes."

Situation decomposition technology is the technology of performing attribute selection and case selection simultaneously from the data structure from which each example constitutes an attribute vector. The newly introduced Matchability criteria are the amount of evaluations which becomes large, when the explanation range of the selected partial information becomes large and a strong relation exists in the inside. Processing of situation decomposition extracts plural partial situations (result of attribute selection and case selection) of corresponding to the local maximum points over this evaluation.

Furthermore, extraction of partial problem space (based on the Markov decision process) is possible using the technology which extended situation decomposition in the direction of time. In action decision task, such as robot control, partial problem space can be assigned as each module of multi-module architecture. Then, it can be efficiently adapted to unknown problem space by combining extracted plural partial problem space.

My strategy for Brain-like processing

Brain has very flexible learning ability.

The intelligent processes which has more flexible learning abilities are more close to real brain processes.

I want introduce learning ability to my system as possible.

Contents

1. Development and Autonomous Learning

2. SOIS (Self-organizing Information Selection) as Pre-task Learning

3. Delivering Matchable Principle

4. Situation Decomposition using Matchability Criterion

5. Application of Situation Decomposition

6. Conclusions & Future works

Autonomous Learning(Framework)

Outline of this talk

Pre-task learning

Self-organizingInformation Selection

Situation decomposition

Task learning

Cognitive Development

Situation Decomposition using Matchability Criterion

Matchable Principle

Matchability Criterion

Development and Autonomous Learning

Two aspects of Development

“Acquired environmental knowledge without particular goals which helps for problem solving for particular goals”→ “Pre-task Learning” in Autonomous Learning

“Calculation process which increases the predictable and/or operable object in the world”→ Enhancing prediction ability

Autonomous Learning: AL Two phases learning (Research in RWC)

Task learningExisting Knowledge

Acquiring environmental

knowledge

General factFor design

Acquiring solution for goal

goal

No reachingover the wall

Acquiringmovable paths

Generatingpath to the goal

Environment is given

Goal is given

Pre-task learning

DevelopmentToday’s Topic

Pre-task Learning helps Task Learning Autonomous Learning (AL)

Pre-task Learning Acquiring environmental knowledge without particular goal.

Task Learning Environmental knowledge speed up aacquiring solution for

goal.

In human: Adult people can solve given task quickly using enviro

nmental knowledge acquired for other goal or without particular goal.

Development ～ Pre-task Learning

DevelopmentToday’s Topic

Research topics for AL

Pre-task Learning (How to acquire environmental knowledge) Situation Decomposition using Matchability criterion

Situation Decomposition is kind of a Self-organizing Information Selection technology.

Task learning (How to use environmental knowledge) CITTA (Cognition based Intelligent Transaction Architecture)

Multi-module architecture which can combining environmental knowledge acquired during Pre-task learning

Cognitive Distance Learning Goal driven problem solver for each environmental knowledge.

DevelopmentToday’s Topic

Overview of Approaching for AL

CITTACombining environmental

knowledge

SituationDecomposition

Acquiring environmental

knowledge

Cognitive Distance Learning

Problem solver for each environmental

knowledge

Architecture

Learningalgorithm

Pre-task Learning Task Learning

SOIS (Self-organizing Information Selection) as Pre-task Learning

SOIS: Self-organizing Information Selection

Process: Selecting plural partial information from data. → “Situation Decomposition”

Criterion: Evaluation for each partial information. → Matchability Criterion

Knowledge = Set of structure.Partial Information = One kind of structure

※ SOIS could be a kind of knowledge acquiring process in development

Situation Decomposition is kind of SOISFor situation decomposition

Partial Information = Situation

Extracting plural situations which are combination of selected attributes and cases from spread sheet.

MS4

attributes

Cas

es

MS1

MS2

MS3

Delivering Matchable Principle

Two aspects of Development

“Acquired environmental knowledge without particular goals which helps solving problem for particular goals”→ “Pre-task Learning” in Autonomous Learning

“Calculation process which increases the predictable and/or operable object in the world”→ Enhancing prediction ability

How to enhance prediction ability

We needs Criterion for selecting situation.We wants to extract local structures.

Multiplex local structure is mixed in

real world data

MS4

MS1MS2

MS3

Situation Decomposition

Deriving Matchable Principal

What is Criterion for each selecting situation.

Matchable principle“Structures where a matching opportunity is

large are selected.”

Extracting structure (knowledge) without

particular goals.

Prediction is based on matching a case with experiences.

Factors in Matchable Principle To increase matching opportunity

Simplicityof Structure

Ockham’s razorMDL ､ AIC

Consistencyfor Data

Coveragefor Data

Our proposedMatchability criterion

Relation in Structure

AccuracyMinimize error

Case-increasingAttribute-increasing

Association rule

SD (Situation Decomposition ) andImplementation

Situation Decomposition

Extracting plural situations which are combination of selected attributes and cases from spread sheet.

Matchability=This criteria evaluates matching opportunity

Matchable Situation = Local maximums of Matchability

MS4

attributes

Cas

es

MS1

MS2

MS3

Formalization: Whole situation and Partial situations

Whole situation J=(D, N) : Contains N attributes and D cases.Attribute selection vector:

d = (d １ , d ２ ,…,dD)

Case selection vector : n = (n1, n2,…,nN) Vector element di,ni are binary indicator of

selection/unselection.

Number of selected attributes: dNumber of selected cases : n

Situation decomposition extracts some matchable situations from whole situation J=(D, N) which potentially contains 2D+N partial situation.

Case selection using Segment spaceSegment space is multiplication of separation of each

selected attributes. (example: two dimension)n ： Number of selected cases

Sd ： Number of total

segments

rd ： Number of

selected segments

※ Cases inside the chosen segments are surely chosen.

Sd =s1 s2

attribute1

attr

ibut

e2

dd SC

rC

NCNSrnM logloglog),,,( 321 dd

[Number of selected cases] n →Make Larger

[Number of total segments] Sd →Make Larger

Matchability criterion from Matchable Principle

nn

Sd

rd

rdN: Total number of cases, C1, C ２ , C ３ : Positive

constant

[Number of selected segments] rd →Make Smaller

Simplicityof Structure

Coveragefor Data

Matchability Focuses in covariance

Types of Relations Coincidence

The relation to which two events happen simultaneously Covariance

The relation that another attribute value will also change if one attribute value changes

Matchability: Estimates covariance

in selected data for categorical attributes.

A B C

ⅰ ８０ １０ １０

ⅱ １０ ８０ １０

ⅲ １０ １０ ８０

How to find situations

Algorithms searches local maximums of Matchability Criterion.

Algorithm Overview for each subset of d of D Search Local maximums Reject saddle point end

Time complexity 2∝ D

Simple example

Input situation Mixture of cases on two

plains. Situation A: x + z = 1 Situation B: y +z = 1

Extracted situation Input Situations

MS １＝ Input Situation A MS ２＝ Input Situation B

A New Situation MS ３ :

line x = y, x + z = 1

Generalization ability Multi-valued function φ:(x,y)→z

Even if the input situation A (x+z=1) lacks half of its parts, such that no data exists in the range y>0.5, our method outputs φMS1(0,1)=1.0.

Applications of Situation Decomposition (SD)

Multi-module Prediction System

Input Output

●Training cases

500 cases are sprayed on each plain in uniform distribution in the range x=[0.0, 1.0] and y=[0.0, 1.0].

●Test cases

11×11 cases are arranged to notches at a regular interval of 0.1 on each plane

Training cases and Test cases

q: sampling rate

1.E- 04

1.E- 03

1.E- 02

1.E- 010 20 40 60 80 100

Sampling rate q

Err

orPrediction Result

without Matchable Situation

with Matchable Situation

Autonomous Learning: AL Two step learning (Research in RWC)

Task learningExisting Knowledge

Acquiring environmental

knowledge

General factFor design

Acquiring solution for goal

goal

No reachingover the wall

Acquiringmovable paths

Generatingpath to the goal

Environment is given

Goal is given

Pre-task learning

DevelopmentToday’s Topic

Demonstration of Autonomous Learning

Door & Key task with CITTA

Start

Mobile Agent

Door

Telephone

Key

Goal

Agent acquire knowledge as situation

Door can open by the key.

Input/Output

Each Situation is used as Module

Position Action Object Belongings

Matchable Situation ｉMatchable Situation ｉ

Matchable Situation ｉMatchable Situation ｉ

Matchable Situation １

Go by wallGo straight

Matchable Situation ２

Open door by telephone

Open door by Key

ExtractingMatchable Situation

Pre-task Learning

Combining Matchable Situation

Task Learning

…

Environment

Mobile Agent

Situation Decomposition in AL

SD in Pre-task learning:Situation decomposition handles input /output

vector of two time step for extracts Markov process.

Advantages by SD in Task learning: Adaptation by combining situations are

possible.Learning data can be reduced, because

learning space for each module is reduced.

Conclusions and Future works

Autonomous Learning

Conclusions

Pre-task learning

Self-organizingInformation Selection

Situation decomposition

Task learning

Cognitive Development

Situation Decomposition using Matchability Criterion

Matchable Principle

Matchability Criterion

Conclusions & Future workSituation decomposition Matchability is new model selection criterion maximizing matching

opportunity, which emphasize Coverage for data. In opposition ockham’s razor emphasize the Consistency for data. Decomposed situations by matchability criterion has powerful prediction ability. Situation decomposition method can be applied to pre-processing of data analysis, self-organization, pattern recognition and so on.

Future work

Situation decomposition: Needs theoretical research on Matchabilty criterion.

This intuitively delivered criterion affected unbalanced data.

Needs speed up for large-scale problem. Exponential time complexity for number of attribute is awful.

Advanced Self-organized Information Selection Situation decomposition method only selects set of attributes

and cases

Autonomous Learning: Relates with the knowledge of cognitive science.