New Bulgarian University MindRACES, First Review Meeting, Lund, 11/01/2006 Anticipation by Analogy An Attempt to Integrate Analogical Reasoning with Perception,

MindRACES, First Review Meeting, Lund, 11/01/2006

New Bulgarian University

Anticipation by AnalogyAnticipation by Analogy

An Attempt to Integrate Analogical Reasoning with Perception, Selective Attention, Context, and Motor Control

An Attempt to Integrate Analogical Reasoning with Perception, Selective Attention, Context, and Motor Control

MindRACES, First Review Meeting, Lund, 11/01/2006 2NBU

Anticipation Mechanisms

• Explicit Anticipation: analogy-making Predictions based on one single example• Implicit Anticipation: context & relevance Predicting relevance based on context –

guiding attention in reasoning and perception• Combining Explicit and Implicit

Anticipation


Examples of Anticipation based on analogy-making and context

• Searching for your keys

They are not at their usual place, so • try to reconstruct what you have done with

them (memory reconstruction), • reminding of old episodes of key search

and where you found them (analogy)• Perceived elements (context) guide the

reconstruction, reminding, and analogy



• Searching for your car in the parking slot

• try to reconstruct where you have parked it (memory reconstruction),

• reminding of old episodes of car search and where you found it (analogy)

• reminding of old episodes of key search and where you found it (remote analogy)

• Perceived elements (context) guide the reconstruction, reminding, and analogy



• Predicting the outcome of a game

• The same as the last outcome• The same as the last failure• The same as the last success• The same as an special old case with this

game• The same as an old case with another game• Perceived elements (context) guide the

reminding and analogy



• Predicting your partner’s or your rival’s next move

• What would I do in this situation (analogy with myself)

• What has this partner/rival done is analogous situation in the past (reminding of specific old case)

• What has another partner/rival done is analogous situation in the past (reminding of specific old case)

• Perceived elements (context) guide the reminding and analogy


Analogy-Making

• Analogy-making is the transfer of a system of relations from one domain (base) to another (target). Similarity based on structure, not overall similarity.

• Analogy is a very basic human ability.


Analogy-Making

in

water

tpot

in

oven

in

milkw

tpot

on

hplate

corr-to

corr-to

corr-to

corr-to

corr-to


Rutherford’s Analogy

Sun Nucleus++

-

-


Rutherford’s analogyRutherford’s analogy

sun

planet

yellow

mass

mass

temperature

greater

color

revolves-around

attractsTsun

Tplanet

Msun

Mplanet

causes

temperature

greater

mass

mass

revolves-around

attractsMnucleus

causes ?greater

nucleus

electron

Melectron

sun

planet

yellow

mass

mass

temperature

greater

color

revolves-around

attractsTsun

Tplanet

Msun

Mplanet

causes

temperature

greater

mass

mass

revolves-around

attractsMnucleus

causes ?greater

nucleus

electron

Melectron

The hydrogen atom is like our solar system.

The Sun has a greater mass than the Earth and attracts it, causing the Earth to revolve around the Sun. The nucleus also has a greater mass then the electron and attracts it. Therefore it is plausible that the electron also revolves around the nucleus.


Main Implementation Tool - AMBR

• AMBR – a cognitive model of human analogy-making.

• The model is hybrid and integrates symbolic processing and connectionist spreading activation and constraint satisfaction at a micro level.

• The model is highly parallel and the behavior of the macro system emerges from the local interactions of micro agents.


Challenges to the pre-existing version of AMBR

• AMBR was a theoretical tool – it was never applied in realistic domain before.

• AMBR was developed for complex problem-solving, not for anticipation.

• AMBR was a model of the mind outside of a body – no interactions with the environment – no perception, no manipulation.

• AMBR was coded in LISP with no possibilities for communications with other software.


Scenario Implementation

• Selection of the scenarios to be used by NBU

• Developing simulation tools• First simulation experiments


Scenarios studied by NBU

• Finding and Looking for an object (finding an object in a single room or in a maze of multiple rooms)

• Guards and thieves (collecting objects which are guarded by other agents)


Rooms layout

Room A Room B

Room C

Room D Room E

Room F

Room G

N


Looking for an Object (Scenario 1)

Room B

Room C

Room D Room E

Room F

Room G

N


Guards and thieves (Scenario 3)

Room C

Room D Room E

Room F

Room G

N

Treasure hunters

Guards


Developing Simulation Tools

• The AMBR model is being further developed and re-implemented in C#.

• The software for AIBO and Pioneer 3 is being mastered and tested.

• The simulation environment WEBOTS 5 is studied and simple simulation of the scenarios are being built.

• A middle tier is being implemented for communication between AMBR on one side and the robots and simulated environment on the other.


Overall System Architecture

Overall System Architecture

Re

aso

ning

tie

rR

eas

oni

ng t

ier

Mid

dle

tier

Mid

dle

tier

Wo

rld ti

er

Wo

rld ti

er

Simulated or Real

Pre-processes data from other tiers

The core of the system

Real robots(AIBO, Pioneer, …)

Webots(Simulated AIBO,

Pioneer, …)

Processes data from and to the world layer.

Processes data from and to the reasoning layer.

DUAL/ AMBRThe analogical machine

Responsible for the inteligent/ anticipatory behaviour of the whole system

Web

ots

API

Mid

dle

tie

r API

Rob

ot s

pec

ific

API

Mid

dle

tie

r API

XM

L St

ruct

ure

d d

ata

WORLD

COMMUNI-CATION

REASONING


World

AIBO ERS7 Webots simulation


Communication

• World tier -> Reasoning tier Collect information about the world

using symbolic data from Webots Report it to the Reasoning layer

in suitable for AMBR form

• Reasoning tier -> World tier Get the motion plan from AMBR:

e.g “Go to the left cube” Send commands for movement to Webots

turning in place, walking forward


Reasoning

• Reasoning by analogy with previous episode (using the AMBR cognitive model)

• Describing AMBR in UML• Implementation of the AMBR model in C#• Project infrastructure (version control, unit

testing, etc.)


Anticipation by Analogy

?


Past Episodes in Robot’s Memory

B1

cube

B2

Unique color

Same color

cylinders

B3

Left-of 3

Left-of 1

Left-of 2

prisms

B4

neighbor

pyramids

Same color

Left-of 1 Left-of 2

Unique color

Left-of 3

Neighbor 1 Neighbor 2

Target situation


Results from the Simulation of Anticipation by Analogy

Same color

Left-of 1 Left-of 2

Unique color

Left-of 3


B1

cube



Same color

Left-of 1 Left-of 2

Unique color

Left-of 3


B2

Unique color

Same color

cylinders



Same color

Left-of 1 Left-of 2

Unique color

Left-of 3


B3

Left-of 3

Left-of 1

Left-of 2

prisms



B4

neighbor

pyramids

Same color

Left-of 1 Left-of 2

Unique color

Left-of 3




Simulation2.1

BASES

B1 B2 B3 B4

Simulation2.1

TARGET

T1

Unique color

Same color

cube cylinders

Left-of 3

Left-of 1

Left-of 2

neighbor

prisms pyramids

balls

Same color

Left-of 1 Left-of 2

Unique color

Left-of 3


B4

neighbor

pyramids



Same color

Left-of 1 Left-of 2

Unique color

Left-of 3


B2

Unique color

Same color

cylinders

Same color

Left-of 1 Left-of 2

Unique color

Left-of 3


B3

Left-of 3

Left-of 1

Left-of 2

prisms

B4

neighbor

pyramids

Same color

Left-of 1 Left-of 2

Unique color

Left-of 3



Simulation Result - Video

Video Clip


Challenges and Problems

• AMBR was developed as a model of complex analogies and therefore fitting and changes were required to produce anticipation: Superficial features such as colors are typically

ignored – colors are important in this domain; Episodes are complex and differ significantly

from each other – episodes are very similar in this domain.



• AMBR was developed as an isolated reasoning model – needs to be integrated into a complete cognitive system: Perceptual abilities need to be integrated that will

encode the target situation – perception of objects, properties and relations – this is solved in the simulation environment, needs to be solved with real robots; integration of symbolic and sub-symbolic approach;

Selective attention needs to be modeled to limit the representation of the target and to focus on certain aspects of the situation;

Motor control – planning and motor control mechanisms



• The simulation results need to be compared and possibly fitted to human data: Some of the simulation data perfectly match

human data; Some differ significantly.


Comparing Simulation and Human Data: 100 Runs on each Target

А B C D

1 2 3 4 5 6 7 8


Comparing Simulation and Human Data: 100 Runs on each Target

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8

A

Б

В

Г

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8

А

Б

В

Г

Simulation data Human data


Integration with work of other partners

• Perception of objects, properties, relations – cooperation with IDSIA, LUCS, ISTC, OFAI

• Selective attention – integration of top-down and bottom-up mechanisms – cooperation with LUCS, IDSIA

• Emotions as regulators of the mechanisms of analogy-making, analogies as source of emotions – cooperation with ISTC, IST


Anticipation by Analogy: Putting things together

Room A Room B

Room C

Room D Room E

Room F

Room G

N

Perception: target representation: IDSIA, LUCS

Motor control: OFAI, IDSIA, LUCS

Selective attention: LUCS, IDSIA, NBU

Emotions (IST, ISTC)

Analogical reasoning (NBU)


Thank you for your attention!

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Documents

New Bulgarian University MindRACES, First Review Meeting, Lund, 11/01/2006 Anticipation by Analogy An Attempt to Integrate Analogical Reasoning with Perception,