the role of confidence factor in “humanizing” the decision making of an ai agent syed...

The role of Confidence Factor in The role of Confidence Factor in “Humanizing” the decision making “Humanizing” the decision making

of an AI Agentof an AI Agent

Syed Enam-ur-RehmanSyed Enam-ur-Rehman11

Mohammed Zeeshan OzairMohammed Zeeshan Ozair22

1Department of Computer Engineering, Sir Syed University of Engineering & Technology, 2The Aga Khan University & Hospital

IntroductionIntroduction

Focus of AI developmentFocus of AI development Usually the decisions of an AI agent are Usually the decisions of an AI agent are

either too calculated or too blunteither too calculated or too blunt A method is proposed for making A method is proposed for making

decisions, which is simple, adaptable and decisions, which is simple, adaptable and endeavors to represent a more realistic endeavors to represent a more realistic humanoid behaviorhumanoid behavior

InspirationInspiration

Situation 1: Situation 1: Unreal Tournament 2003 (an action game)Unreal Tournament 2003 (an action game)

Situation 2: Situation 2: Fifa 2002 (a soccer game)Fifa 2002 (a soccer game)

Decision TheoryDecision Theory

K possible mastery states that take on values mK possible mastery states that take on values mkk

Z the response vector composed of zZ the response vector composed of z11, z, z22, … z, … zii

N

1ikik )m|P(z)m|P(z

)P(m)m|P(zz)|P(m kkk c

……continuedcontinued

Likelihood ratio L(z) is compared with a Likelihood ratio L(z) is compared with a criterion variable criterion variable

)m|P(z)m|P(z

L(z)1

2

)(

)(

1

2

zLifd

zLifd

Rule-based SystemsRule-based Systems

For example: qp )(P1

pr )P( 2

sr )P( 3

q)c(

Curtain RaiserCurtain Raiser

The complexity of Decision Theory makes The complexity of Decision Theory makes it inaccessible to manyit inaccessible to many

Rule-based systems lack the fidelity of Rule-based systems lack the fidelity of Decision Theory and are dependant on Decision Theory and are dependant on long term knowledgelong term knowledge

Neither of these model human behaviors Neither of these model human behaviors accuratelyaccurately

……continuedcontinued

Decision making based on confidence Decision making based on confidence theory is relatively simple, flexible and theory is relatively simple, flexible and reasonably adaptable, yet trying to reasonably adaptable, yet trying to achieve functionalityachieve functionality

It attempts to model human behavior more It attempts to model human behavior more accuratelyaccurately

Emotions such as anger, revenge, trust, Emotions such as anger, revenge, trust, mistrust, fear, pride, all an integral part of mistrust, fear, pride, all an integral part of human behavior can be expressedhuman behavior can be expressed

Part I: Decision, Confidence and Part I: Decision, Confidence and MotivationMotivation

MCD Where, D = Decision D

M = Motivation M

C = Confidence C

Decision SelectionDecision Selection

maxmax MCMC

D

Diftakennot

Diftaken

For do or die situations…For do or die situations…

maxmax )1()1(CM

……continuedcontinued

Where, = Criterion variable 10

D = Decision 11 D

C = Confidence maxmax CCC M = Motivation maxmax MMM Cmax = Maximum confidence maxC Mmax = Maximum motivation maxM

Part II: MotivationPart II: Motivation

Defined as the need of performing an actDefined as the need of performing an act Is a function of internal and external Is a function of internal and external

factorsfactors Directs goal focused behaviorDirects goal focused behavior Modulated by learning mechanismsModulated by learning mechanisms For e.g., acquiring first-aid with low health, For e.g., acquiring first-aid with low health,

gaining revenge if provided incentive, gaining revenge if provided incentive, aggression if time is running outaggression if time is running out

Part III: ConfidencePart III: Confidence

Where, C = Confidence C

Co = Baseline confidence 0C

∆C = Change in confidence C

CCC 0

Confidence FactorConfidence Factor

0)(

nnrLESPC

0)(

nnrLESKPC

Where, E = Experience 0E

L = Luck L

S = Success S

rn = Resource variable nr ∆C = Change in confidence C

K = Divine constant

0)(

nnrLESKPC

SuccessSuccess

A desired completion of a task or an A desired completion of a task or an accomplishmentaccomplishment

Incremented on each success event with a Incremented on each success event with a scale proportionate to the complexity of scale proportionate to the complexity of the taskthe task

k

kscale

sSS

sssS

0

10 ,...,,

ExperienceExperience

Number of decision cycles involved in Number of decision cycles involved in performing an actperforming an act

Its inclusion ensures,Its inclusion ensures, Ability to induce frustrationAbility to induce frustration An indirect appreciation of any learning An indirect appreciation of any learning

mechanism if presentmechanism if present

LuckLuck

A random event which can affect the A random event which can affect the completion of a taskcompletion of a task

Consanguinity of such events affect Consanguinity of such events affect confidenceconfidence

Overall luck is updated on occurrence of Overall luck is updated on occurrence of each pre-defined luck event with a scale each pre-defined luck event with a scale proportionate to its impactproportionate to its impact

k

kscale

lLL

lllL

0

10 ,...,,

ResourcesResources

These are environmental variables whose These are environmental variables whose availability or deficiency play a significant availability or deficiency play a significant role in completing a taskrole in completing a task

nrrrR ,...,, 10Where, R Є Resources rn = Resource - rn

PersonalityPersonality

Introduces an entropy in the interpretation Introduces an entropy in the interpretation of confidenceof confidence

Allows a spectrum of personalities Allows a spectrum of personalities differentiating timid from aggressivedifferentiating timid from aggressive

PostulatesPostulates

When L = 0,When L = 0,

When E = 0,When E = 0,

When S = 0,When S = 0,

0)(

nnrESKPC

0)(

nnrLSKPC

0nnrKPC

……continuedcontinued

C < 0 if and only if,C < 0 if and only if,

)(,000

LESrrn

nn

n

)(,00

LESrSn

n

weightageweightage

weightageweightage

weightageweightage

weightage

weightage

SP

weightageweightage

SP

weightageweightage

SP

weightage

nn

P

weightage

P

CREL

CREE

CRS

RR

CRR

CP

11

maxmax

11

maxmax

1

maxmax

0maxmax

1

maxmax

maxmax

]))(1)[(1(

]))(1[(

]))(1[(

)(

)(

maxmaxmaxmaxmaxmax )( RLESKPC

Decision ArchitectureDecision Architecture

RecallRecall

Situation 1: Situation 1: Unreal Tournament 2003 (an action game)Unreal Tournament 2003 (an action game)

Situation 2: Situation 2: Fifa 2002 (a soccer game)Fifa 2002 (a soccer game)

Simulation ResultsSimulation Results

Future Scope / ConclusionFuture Scope / Conclusion

Based on simulation results and logical Based on simulation results and logical flow, we believe that this technique has a flow, we believe that this technique has a prospect in AI applications particularly prospect in AI applications particularly those where simulation of humanoid those where simulation of humanoid behavior is requiredbehavior is required

Our immediate focus is the implementation Our immediate focus is the implementation of the proposed architecture into SOAR of the proposed architecture into SOAR which would provide a test-bed for which would provide a test-bed for relatively complex applicationsrelatively complex applications

Emotion & IntellectEmotion & Intellectbyby

Michael BonnellMichael Bonnell

GAME OVERGAME OVER