The role of Confidence Factor in “Humanizing” the decision making of an AI Agent Syed...
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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
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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
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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)
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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
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……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
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Rule-based SystemsRule-based Systems
For example: qp )(P1
pr )P( 2
sr )P( 3
q)c(
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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
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……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
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Part I: Decision, Confidence and Part I: Decision, Confidence and MotivationMotivation
MCD Where, D = Decision D
M = Motivation M
C = Confidence C
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Decision SelectionDecision Selection
maxmax MCMC
D
Diftakennot
Diftaken
For do or die situations…For do or die situations…
maxmax )1()1(CM
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……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
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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
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Part III: ConfidencePart III: Confidence
Where, C = Confidence C
Co = Baseline confidence 0C
∆C = Change in confidence C
CCC 0
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Confidence FactorConfidence Factor
0)(
nnrLESPC
0)(
nnrLESKPC
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Where, E = Experience 0E
L = Luck L
S = Success S
rn = Resource variable nr ∆C = Change in confidence C
K = Divine constant
0)(
nnrLESKPC
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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 ,...,,
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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
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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 ,...,,
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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
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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
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PostulatesPostulates
When L = 0,When L = 0,
When E = 0,When E = 0,
When S = 0,When S = 0,
0)(
nnrESKPC
0)(
nnrLSKPC
0nnrKPC
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……continuedcontinued
C < 0 if and only if,C < 0 if and only if,
)(,000
LESrrn
nn
n
)(,00
LESrSn
n
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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
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Decision ArchitectureDecision Architecture
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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)
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Simulation ResultsSimulation Results
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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
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Emotion & IntellectEmotion & Intellectbyby
Michael BonnellMichael Bonnell
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GAME OVERGAME OVER