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Arti� ial Immune SystemsTheory and Appli ationsAshwin Pan hapakesanOttawa Carleton Institute for Computer S ien eUniversity of OttawaEvolutionary Programming and Arti� ial LifeAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 1 / 58
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Outline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 2 / 58
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Some Biology The Two-Tier DefenseOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 3 / 58
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Some Biology The Two-Tier DefenseThe Two-Tier DefenseWhite Blood CellsInnate Immune SystemSentry a la Big BrotherAdaptive Immune Systema la memoryFun tion of previous exposureSpea ialized training, but a Ja k of few trades (B- ells)evolutionaryAlarm sirens (T- ells)not evolutionaryalert and e�e t proliferation of (the appropriate) B- ellsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 4 / 58
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Some Biology The Two-Tier DefenseThe Two-Tier DefenseWhite Blood CellsInnate Immune SystemSentry a la Big BrotherAdaptive Immune Systema la memoryFun tion of previous exposureSpea ialized training, but a Ja k of few trades (B- ells)evolutionaryAlarm sirens (T- ells)not evolutionaryalert and e�e t proliferation of (the appropriate) B- ellsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 4 / 58
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Some Biology The Two-Tier DefenseThe Two-Tier DefenseWhite Blood CellsInnate Immune SystemSentry a la Big BrotherAdaptive Immune Systema la memoryFun tion of previous exposureSpea ialized training, but a Ja k of few trades (B- ells)evolutionaryAlarm sirens (T- ells)not evolutionaryalert and e�e t proliferation of (the appropriate) B- ellsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 4 / 58
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Some Biology The Two-Tier DefenseThe Two-Tier DefenseWhite Blood CellsInnate Immune SystemSentry a la Big BrotherAdaptive Immune Systema la memoryFun tion of previous exposureSpea ialized training, but a Ja k of few trades (B- ells)evolutionaryAlarm sirens (T- ells)not evolutionaryalert and e�e t proliferation of (the appropriate) B- ellsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 4 / 58
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Some Biology Clonal Sele tionOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 5 / 58
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Some Biology Clonal Sele tionClonal Sele tionTheoryNew antigen (Agi )Fitness fun tion: antibody(Abk ) � how exa tly do you re ognize Agi?Proliferability ∝ �tness�tness = 0 � Abk dies
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 6 / 58
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Some Biology Clonal Sele tionClonal Sele tionTheoryNew antigen (Agi )Fitness fun tion: antibody(Abk ) � how exa tly do you re ognize Agi?Proliferability ∝ �tness�tness = 0 � Abk dies
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 6 / 58
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Some Biology Clonal Sele tionClonal Sele tionTheoryNew antigen (Agi )Fitness fun tion: antibody(Abk ) � how exa tly do you re ognize Agi?Proliferability ∝ �tness�tness = 0 � Abk dies
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 6 / 58
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Some Biology Clonal Sele tionClonal Sele tion ( ontd.)High-Level Algorithm
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 7 / 58
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Some Biology Negative Sele tionOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 8 / 58
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Some Biology Negative Sele tionNegative Sele tionTheoryMy Immune System should prote t me, not atta k me (autoimmunediseases)eliminate self dete ting antibodies
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 9 / 58
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Some Biology Positive Sele tionOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 10 / 58
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Some Biology Positive Sele tionPositive Sele tionTheoryFor e�e tiveness and e� ien yeliminate antibodies that don't mat h any antigensNow we have a highly trained team of antibodies that respond to allpreviously seen antigensa la Spe OpsSubsequent atta k by the same antigensQui ker, more e�e tive responseMemory ells are not 100% exa t, so they an ope with some mutation... like studying omputer s ien e and then getting a jobAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 11 / 58
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Some Biology Positive Sele tionPositive Sele tionTheoryFor e�e tiveness and e� ien yeliminate antibodies that don't mat h any antigensNow we have a highly trained team of antibodies that respond to allpreviously seen antigensa la Spe OpsSubsequent atta k by the same antigensQui ker, more e�e tive responseMemory ells are not 100% exa t, so they an ope with some mutation... like studying omputer s ien e and then getting a jobAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 11 / 58
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Some Biology Positive Sele tionPositive Sele tionTheoryFor e�e tiveness and e� ien yeliminate antibodies that don't mat h any antigensNow we have a highly trained team of antibodies that respond to allpreviously seen antigensa la Spe OpsSubsequent atta k by the same antigensQui ker, more e�e tive responseMemory ells are not 100% exa t, so they an ope with some mutation... like studying omputer s ien e and then getting a jobAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 11 / 58
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Some Biology Self/Non-Self Dis riminationOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 12 / 58
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Some Biology Self/Non-Self Dis riminationThree Classes of RepertoiresRepertoiresNeed antibodies that an identify as many di�erent antigens aspossibleSimultaneously, don't atta k the hostNot atta king the host always wins the tradeo� (else, Lupus)Potential Repertoire:Spannning set of antibodies that an possibly be generated.By expression and/or by mutationAvailable Repertoire (expressed Repertoire):Antibodies that already exist.These an be made to multiply and deployed as requiredIn a way, this de�nes the spanning set in the Potential RepertoireA tual Repertoire:Antibodies on the front linesCurrently �ghting infe tionDoes not in lude Innate antibodiesAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 13 / 58
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Some Biology Self/Non-Self Dis riminationThree Classes of RepertoiresRepertoiresNeed antibodies that an identify as many di�erent antigens aspossibleSimultaneously, don't atta k the hostNot atta king the host always wins the tradeo� (else, Lupus)Potential Repertoire:Spannning set of antibodies that an possibly be generated.By expression and/or by mutationAvailable Repertoire (expressed Repertoire):Antibodies that already exist.These an be made to multiply and deployed as requiredIn a way, this de�nes the spanning set in the Potential RepertoireA tual Repertoire:Antibodies on the front linesCurrently �ghting infe tionDoes not in lude Innate antibodiesAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 13 / 58
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Some Biology Self/Non-Self Dis riminationThree Classes of RepertoiresRepertoiresNeed antibodies that an identify as many di�erent antigens aspossibleSimultaneously, don't atta k the hostNot atta king the host always wins the tradeo� (else, Lupus)Potential Repertoire:Spannning set of antibodies that an possibly be generated.By expression and/or by mutationAvailable Repertoire (expressed Repertoire):Antibodies that already exist.These an be made to multiply and deployed as requiredIn a way, this de�nes the spanning set in the Potential RepertoireA tual Repertoire:Antibodies on the front linesCurrently �ghting infe tionDoes not in lude Innate antibodiesAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 13 / 58
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Some Biology Self/Non-Self Dis riminationThree Classes of RepertoiresRepertoiresNeed antibodies that an identify as many di�erent antigens aspossibleSimultaneously, don't atta k the hostNot atta king the host always wins the tradeo� (else, Lupus)Potential Repertoire:Spannning set of antibodies that an possibly be generated.By expression and/or by mutationAvailable Repertoire (expressed Repertoire):Antibodies that already exist.These an be made to multiply and deployed as requiredIn a way, this de�nes the spanning set in the Potential RepertoireA tual Repertoire:Antibodies on the front linesCurrently �ghting infe tionDoes not in lude Innate antibodiesAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 13 / 58
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Some Biology Immune Network TheoryOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 14 / 58
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Some Biology Immune Network TheoryImmune Network TheoryNeed antibodies that an re ognize as many di�erent antigens aspossiblePerfe t mat hing not required for re ognitionshape spa e and toleran e thresholdImperfe tness + low threshold = ability to dete t wider range ofantigenstradeo�: high orrelation between re ognizing more antigens andprobability of dete ting self
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 15 / 58
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Some Biology Immune Network TheoryImmune Network TheoryNeed antibodies that an re ognize as many di�erent antigens aspossiblePerfe t mat hing not required for re ognitionshape spa e and toleran e thresholdImperfe tness + low threshold = ability to dete t wider range ofantigenstradeo�: high orrelation between re ognizing more antigens andprobability of dete ting self
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 15 / 58
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Some Biology Immune Network TheoryImmune Network TheoryAutonomously regulates and maintains immune network within arange of a tivityToo high � possibility of autoimmuneToo low � possibility of not good enough responseContinuous produ tion of new elementsRe ognize previously seen antigens betterRe ognize possibly unseen antigensTradeo�: re ognition vs. Positive Sele tion
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 16 / 58
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Some Biology Immune Network TheoryImmune Network TheoryAutonomously regulates and maintains immune network within arange of a tivityToo high � possibility of autoimmuneToo low � possibility of not good enough responseContinuous produ tion of new elementsRe ognize previously seen antigens betterRe ognize possibly unseen antigensTradeo�: re ognition vs. Positive Sele tion
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 16 / 58
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Some Biology Immune Network TheoryImmune Network TheoryStru tureDes ribes types of intera tions among omponentsNo regard for onsequen es of intera tionsPossible representation: matrix onne tivity a la ANNHigh orrelation to immune memoryevolution of antibodiesSpeaks of omponent intera tions, a tive elementsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 17 / 58
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Some Biology Immune Network TheoryImmune Network TheoryStru tureDes ribes types of intera tions among omponentsNo regard for onsequen es of intera tionsPossible representation: matrix onne tivity a la ANNHigh orrelation to immune memoryevolution of antibodiesSpeaks of omponent intera tions, a tive elementsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 17 / 58
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Some Biology Immune Network TheoryImmune Network TheoryStru tureDes ribes types of intera tions among omponentsNo regard for onsequen es of intera tionsPossible representation: matrix onne tivity a la ANNHigh orrelation to immune memoryevolution of antibodiesSpeaks of omponent intera tions, a tive elementsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 17 / 58
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Some Biology Immune Network TheoryImmune Network TheoryStru tureDes ribes types of intera tions among omponentsNo regard for onsequen es of intera tionsPossible representation: matrix onne tivity a la ANNHigh orrelation to immune memoryevolution of antibodiesSpeaks of omponent intera tions, a tive elementsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 17 / 58
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Some Biology Immune Network TheoryImmune Network TheoryDynami sVariations overtime on entration of whi h antibodiesa�nities of antibodiesenvironment∂ immuneComponenets
∂environmentI grew up in IndiaI've lived in Canada sin e 2006Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 18 / 58
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Some Biology Immune Network TheoryImmune Network TheoryDynami sVariations overtime on entration of whi h antibodiesa�nities of antibodiesenvironment∂ immuneComponenets
∂environmentI grew up in IndiaI've lived in Canada sin e 2006Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 18 / 58
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Some Biology Immune Network TheoryImmune Network TheoryMetaDynami sHow are new antibodies re ruitedAny Ab an intera t with the a tual repertoireKept or destroyed ( lonal/negative sele tion)AKA immune re ruitment me hanismimportant for overing new areas in antigen spa eIndu tion based on sensitivitya�nity of element to pre-existing elementsproblem spe i� usage of a�nityAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 19 / 58
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Some Biology Immune Network TheoryImmune Network TheoryMetaDynami sHow are new antibodies re ruitedAny Ab an intera t with the a tual repertoireKept or destroyed ( lonal/negative sele tion)AKA immune re ruitment me hanismimportant for overing new areas in antigen spa eIndu tion based on sensitivitya�nity of element to pre-existing elementsproblem spe i� usage of a�nityAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 19 / 58
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Some Biology Immune Network TheoryImmune Network TheoryMetaDynami sHow are new antibodies re ruitedAny Ab an intera t with the a tual repertoireKept or destroyed ( lonal/negative sele tion)AKA immune re ruitment me hanismimportant for overing new areas in antigen spa eIndu tion based on sensitivitya�nity of element to pre-existing elementsproblem spe i� usage of a�nityAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 19 / 58
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Why the Immune System Why the Immune SystemOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 20 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemPattern Re ognitionAll antigens are ve tors in an N-D shape spa eBinaryHammingEu lideanIntegerReal valuedSymboli (generalized)
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 21 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemUniqueness> 7 billion people on this planetEa h one has a di�erent immune systemGeneralizable into di�erent problems
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 22 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemSelf IdentityHost (self) is always prote tedNegative Sele tion ensures thisNote: People do still get lupus
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 23 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemSelf IdentityHost (self) is always prote tedNegative Sele tion ensures thisNote: People do still get lupus
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 23 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemDiversity and DiespensibilityCan use a wide range of parts to build immune systemsComponents are dispensibleLoss of one WBC is not a major lossClonal proliferation
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 24 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemDiversity and DiespensibilityCan use a wide range of parts to build immune systemsComponents are dispensibleLoss of one WBC is not a major lossClonal proliferation
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 24 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemAutonomy and CooperationNo entral de ision making ontrolChemi al signals from all sentries and soldiersEa h omponent ommuni ates with the relevant othersRelevant omponents jump into a tion as requiredAutonomy gives way to self organization a la emergent behavior
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 25 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemAutonomy and CooperationNo entral de ision making ontrolChemi al signals from all sentries and soldiersEa h omponent ommuni ates with the relevant othersRelevant omponents jump into a tion as requiredAutonomy gives way to self organization a la emergent behavior
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 25 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemAutonomy and CooperationNo entral de ision making ontrolChemi al signals from all sentries and soldiersEa h omponent ommuni ates with the relevant othersRelevant omponents jump into a tion as requiredAutonomy gives way to self organization a la emergent behavior
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 25 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemAnomaly Dete tionVery good at anomaly dete tionIf I an't re ognize you, you must be anomalousNoise toleran eA tual Repertoire keeps hanging in dynami waysPotential repertoireDi�erent forms of proliferation, mutation, rossoverDistributability and interoperability
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 26 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemAnomaly Dete tionVery good at anomaly dete tionIf I an't re ognize you, you must be anomalousNoise toleran eA tual Repertoire keeps hanging in dynami waysPotential repertoireDi�erent forms of proliferation, mutation, rossoverDistributability and interoperability
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 26 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemAnomaly Dete tionVery good at anomaly dete tionIf I an't re ognize you, you must be anomalousNoise toleran eA tual Repertoire keeps hanging in dynami waysPotential repertoireDi�erent forms of proliferation, mutation, rossoverDistributability and interoperability
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 26 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemAnomaly Dete tionVery good at anomaly dete tionIf I an't re ognize you, you must be anomalousNoise Toleran eA tual Repertoire keeps hanging in dynami waysA tive repertoire hanges based on antigenMethod of proliferation hanges based on e�e tiveness
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 27 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemAnomaly Dete tionVery good at anomaly dete tionIf I an't re ognize you, you must be anomalousNoise Toleran eA tual Repertoire keeps hanging in dynami waysA tive repertoire hanges based on antigenMethod of proliferation hanges based on e�e tiveness
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 27 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemResilian eS ales by available energyNot a binary thresholdFault Toleran eRemoving the spe ops will not ripple the systemOther omponents will �re-evolve� and spe ialize into those spe ops
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 28 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemResilian eS ales by available energyNot a binary thresholdFault Toleran eRemoving the spe ops will not ripple the systemOther omponents will �re-evolve� and spe ialize into those spe ops
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 28 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemLearning and MemoryEvolution is powerfulqui k to adapt su� iently to new invadersDefense again repeat atta ks is qui ker and more e� ientStill evolves, but from a better starting point
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 29 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemLearning and MemoryEvolution is powerfulqui k to adapt su� iently to new invadersDefense again repeat atta ks is qui ker and more e� ientStill evolves, but from a better starting point
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 29 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemInteroperabilityIndependant, autonomousCooperative - ommuni ates to other parts of the hostImmune system is ross ompatible with other hosts of the same spe iesva ines
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 30 / 58
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Why the Immune System Why the Immune SystemWhy the Immune SystemInteroperabilityIndependant, autonomousCooperative - ommuni ates to other parts of the hostImmune system is ross ompatible with other hosts of the same spe iesva ines
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 30 / 58
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Framework for Engineering AIS The TheoryOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 31 / 58
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Framework for Engineering AIS The TheoryIdentify the ProblemWhi h prin iples from biologi al analogy apply to what you're tryingto doAIS is predominantly pattern mat hing - so why not use Neural Nets?Parameters:Representation of problemWhat is an antigenWhat is an antibodyWhat types of antibodies to useA�nity thresholdAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 32 / 58
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Framework for Engineering AIS The TheoryIdentify the ProblemWhi h prin iples from biologi al analogy apply to what you're tryingto doAIS is predominantly pattern mat hing - so why not use Neural Nets?Parameters:Representation of problemWhat is an antigenWhat is an antibodyWhat types of antibodies to useA�nity thresholdAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 32 / 58
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Framework for Engineering AIS The TheoryIdentify the ProblemWhi h prin iples from biologi al analogy apply to what you're tryingto doAIS is predominantly pattern mat hing - so why not use Neural Nets?Parameters:Representation of problemWhat is an antigenWhat is an antibodyWhat types of antibodies to useA�nity thresholdAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 32 / 58
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Framework for Engineering AIS The TheoryIdentify the ProblemContinuedSize of available repertoireStrategy of Evolving the AISMutation operations, probabilityMating operationsReverse MappingOutputs from AIS domainMap AIS domain outputs to desired domain
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 33 / 58
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Framework for Engineering AIS The TheoryIdentify the ProblemContinuedSize of available repertoireStrategy of Evolving the AISMutation operations, probabilityMating operationsReverse MappingOutputs from AIS domainMap AIS domain outputs to desired domain
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 33 / 58
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Framework for Engineering AIS The TheoryIdentify the ProblemContinuedSize of available repertoireStrategy of Evolving the AISMutation operations, probabilityMating operationsReverse MappingOutputs from AIS domainMap AIS domain outputs to desired domain
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 33 / 58
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Framework for Engineering AIS An ExampleOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 34 / 58
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Framework for Engineering AIS An ExampleAn ExampleProblem Identify bla k-and-white pi tures of �1� and �4�Shape Spa e Binary Hamming (0 = white pixel, 1 = bla k pixel)Sele tion Method Negative sele tion or Clonal sele tionCross Rea tivity Threshold 1 (at most 1 ommon bit is tolerated for amat h)Distan e Measure ∑Li=0 dist(i), where dist(i) = 1 if Agi 6= Abi else 0
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 35 / 58
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Framework for Engineering AIS An ExampleAn ExampleProblem Identify bla k-and-white pi tures of �1� and �4�Shape Spa e Binary Hamming (0 = white pixel, 1 = bla k pixel)Sele tion Method Negative sele tion or Clonal sele tionCross Rea tivity Threshold 1 (at most 1 ommon bit is tolerated for amat h)Distan e Measure ∑Li=0 dist(i), where dist(i) = 1 if Agi 6= Abi else 0
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 35 / 58
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Framework for Engineering AIS An ExampleAn ExampleProblem Identify bla k-and-white pi tures of �1� and �4�Shape Spa e Binary Hamming (0 = white pixel, 1 = bla k pixel)Sele tion Method Negative sele tion or Clonal sele tionCross Rea tivity Threshold 1 (at most 1 ommon bit is tolerated for amat h)Distan e Measure ∑Li=0 dist(i), where dist(i) = 1 if Agi 6= Abi else 0
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 35 / 58
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Framework for Engineering AIS An ExampleAn ExampleProblem Identify bla k-and-white pi tures of �1� and �4�Shape Spa e Binary Hamming (0 = white pixel, 1 = bla k pixel)Sele tion Method Negative sele tion or Clonal sele tionCross Rea tivity Threshold 1 (at most 1 ommon bit is tolerated for amat h)Distan e Measure ∑Li=0 dist(i), where dist(i) = 1 if Agi 6= Abi else 0
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 35 / 58
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Framework for Engineering AIS An ExampleAn ExampleProblem Identify bla k-and-white pi tures of �1� and �4�Shape Spa e Binary Hamming (0 = white pixel, 1 = bla k pixel)Sele tion Method Negative sele tion or Clonal sele tionCross Rea tivity Threshold 1 (at most 1 ommon bit is tolerated for amat h)Distan e Measure ∑Li=0 dist(i), where dist(i) = 1 if Agi 6= Abi else 0
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 35 / 58
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Framework for Engineering AIS An ExampleAn ExampleRe ognition in Binary Hamming Shape Spa eThe orresponding bits of the antigen and antibody have to be omplementaryLike a key �ts the tumblers in a lo kSimilar bits in similar positions ause repulsion and dete tion isredu edCross rea tivity threshold de�nes how mu h repulsion an be tolerated
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 36 / 58
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Framework for Engineering AIS An ExampleAn ExampleRe ognition in Binary Hamming Shape Spa eThe orresponding bits of the antigen and antibody have to be omplementaryLike a key �ts the tumblers in a lo kSimilar bits in similar positions ause repulsion and dete tion isredu edCross rea tivity threshold de�nes how mu h repulsion an be tolerated
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 36 / 58
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Framework for Engineering AIS An ExampleAn ExampleNegative Sele tionS = [ 010 010 010 010101 101 111 001 ]
P =
101 101 101 101010 010 000 110110 010 010 010101 001 101 101111 101 111 001
M = [ 12 2 1 11 92 12 9 3 1 ]Mx,y des ribes the a�nity of Sx to PyAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 37 / 58
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Framework for Engineering AIS An ExampleAn ExampleNegative Sele tionS = [ 010 010 010 010101 101 111 001 ]
P =
101 101 101 101010 010 000 110110 010 010 010101 001 101 101111 101 111 001
M = [ 12 2 1 11 92 12 9 3 1 ]Mx,y des ribes the a�nity of Sx to PyAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 37 / 58
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Framework for Engineering AIS An ExampleAn ExampleNegative Sele tionS = [ 010 010 010 010101 101 111 001 ]
P =
101 101 101 101010 010 000 110110 010 010 010101 001 101 101111 101 111 001
M = [ 12 2 1 11 92 12 9 3 1 ]Mx,y des ribes the a�nity of Sx to PyAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 37 / 58
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Framework for Engineering AIS An ExampleNegative Sele tionNegative Sele tion ContinuedKill all antibodies that re ognize prote ted stringsM = [ 12 2 1 11 92 12 9 3 1 ]P1, P4 re ognize S1P2 re ognizes S2A = P \ {P1, P2, P4} = {P3, P5}
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 38 / 58
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Framework for Engineering AIS An ExampleNegative Sele tionNegative Sele tion ContinuedKill all antibodies that re ognize prote ted stringsM = [ 12 2 1 11 92 12 9 3 1 ]P1, P4 re ognize S1P2 re ognizes S2A = P \ {P1, P2, P4} = {P3, P5}
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 38 / 58
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Framework for Engineering AIS An ExampleNegative Sele tionNegative Sele tion ContinuedKill all antibodies that re ognize prote ted stringsM = [ 12 2 1 11 92 12 9 3 1 ]P1, P4 re ognize S1P2 re ognizes S2A = P \ {P1, P2, P4} = {P3, P5}
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 38 / 58
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Framework for Engineering AIS An ExampleAn ExampleClonal Sele tionS = [ 010 010 010 010101 101 111 001 ]
P =
101 101 101 101010 010 000 110110 010 010 010101 001 101 101111 101 111 001
M = [ 12 2 1 11 92 12 9 3 1 ]Parametersn1 = 3n2 = n3 = 1Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 39 / 58
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Framework for Engineering AIS An ExampleAn ExampleClonal Sele tionS = [ 010 010 010 010101 101 111 001 ]
P =
101 101 101 101010 010 000 110110 010 010 010101 001 101 101111 101 111 001
M = [ 12 2 1 11 92 12 9 3 1 ]Parametersn1 = 3n2 = n3 = 1Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 39 / 58
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Framework for Engineering AIS An ExampleAn ExampleClonal Sele tionS = [ 010 010 010 010101 101 111 001 ]
P =
101 101 101 101010 010 000 110110 010 010 010101 001 101 101111 101 111 001
M = [ 12 2 1 11 92 12 9 3 1 ]Parametersn1 = 3n2 = n3 = 1Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 39 / 58
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Framework for Engineering AIS An ExampleAn ExampleClonal Sele tionS = [ 010 010 010 010101 101 111 001 ]
P =
101 101 101 101010 010 000 110110 010 010 010101 001 101 101111 101 111 001
M = [ 12 2 1 11 92 12 9 3 1 ]Parametersn1 = 3n2 = n3 = 1Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 39 / 58
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Framework for Engineering AIS An ExampleClonal Sele tionStep 3, 4Sele t n1 highest a�nity elements of P.P1> P3> P5A�nity Proportional Reprodu tionP1 gets 3 lonesP3 gets 2 onesP5 gets 1 lone
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 40 / 58
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Framework for Engineering AIS An ExampleClonal Sele tionStep 3, 4Sele t n1 highest a�nity elements of P.P1> P3> P5A�nity Proportional Reprodu tionP1 gets 3 lonesP3 gets 2 onesP5 gets 1 lone
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 40 / 58
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Framework for Engineering AIS An ExampleClonal Sele tionStep 5(inverse) A�nity proportional mutationP1> P3> P5P1 is perfe t, so no mutationP3 gets some mutationP5 gets more mutation
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 41 / 58
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Framework for Engineering AIS An ExampleClonal Sele tionStep 5(inverse) A�nity proportional mutationP1> P3> P5P1 is perfe t, so no mutationP3 gets some mutationP5 gets more mutation
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 41 / 58
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Framework for Engineering AIS An ExampleClonal Sele tionStep 5(inverse) A�nity proportional mutationP1> P3> P5P1 is perfe t, so no mutationP3 gets some mutationP5 gets more mutation
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 41 / 58
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Framework for Engineering AIS An ExampleClonal Sele tionStep 5(inverse) A�nity proportional mutationP1> P3> P5P1 is perfe t, so no mutationP3 gets some mutationP5 gets more mutation
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 41 / 58
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Framework for Engineering AIS An ExampleClonal Sele tionStep 5(inverse) A�nity proportional mutationP1> P3> P5P1 is perfe t, so no mutationP3 gets some mutationP5 gets more mutation
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 41 / 58
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Appli ations Computer Se urityOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 42 / 58
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Appli ations Computer Se urityComputational Se urityMalware Dete tionEasy to identify byte- ode of a new omputerAll built-insOSDriversWhat happens when I install MS Word?An AIS should �nd all and only malwareRe ognize virulent signatureDe oy programs to apture virusesBe areful - don't re ognize de oy programsSignature extra torDatabase of known virusesa la memory ellsBetter than biologyUse de oy programs to learn virulent behavior.Fix a�e ted non-de oy �lesAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 43 / 58
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Appli ations Computer Se urityComputational Se urityMalware Dete tionEasy to identify byte- ode of a new omputerAll built-insOSDriversWhat happens when I install MS Word?An AIS should �nd all and only malwareRe ognize virulent signatureDe oy programs to apture virusesBe areful - don't re ognize de oy programsSignature extra torDatabase of known virusesa la memory ellsBetter than biologyUse de oy programs to learn virulent behavior.Fix a�e ted non-de oy �lesAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 43 / 58
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Appli ations Computer Se urityComputational Se urityMalware Dete tionEasy to identify byte- ode of a new omputerAll built-insOSDriversWhat happens when I install MS Word?An AIS should �nd all and only malwareRe ognize virulent signatureDe oy programs to apture virusesBe areful - don't re ognize de oy programsSignature extra torDatabase of known virusesa la memory ellsBetter than biologyUse de oy programs to learn virulent behavior.Fix a�e ted non-de oy �lesAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 43 / 58
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Appli ations Computer Se urityComputational Se urityMalware Dete tionEasy to identify byte- ode of a new omputerAll built-insOSDriversWhat happens when I install MS Word?An AIS should �nd all and only malwareRe ognize virulent signatureDe oy programs to apture virusesBe areful - don't re ognize de oy programsSignature extra torDatabase of known virusesa la memory ellsBetter than biologyUse de oy programs to learn virulent behavior.Fix a�e ted non-de oy �lesAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 43 / 58
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Appli ations Computer Se urityComputational Se urityMalware Dete tionEasy to identify byte- ode of a new omputerAll built-insOSDriversWhat happens when I install MS Word?An AIS should �nd all and only malwareRe ognize virulent signatureDe oy programs to apture virusesBe areful - don't re ognize de oy programsSignature extra torDatabase of known virusesa la memory ellsBetter than biologyUse de oy programs to learn virulent behavior.Fix a�e ted non-de oy �lesAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 43 / 58
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Appli ations Walking RobotsOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 44 / 58
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Appli ations Walking RobotsGait A quisitionIshiguro et al. on HexapodsWalking requires legs to move with proper relative phase shiftensure equal weight distribution on all a tive legsAntigen = load on a legAntibody = movement of legB-CellOne for ea h leg
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 45 / 58
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Appli ations Walking RobotsGait A quisitionIshiguro et al. on HexapodsWalking requires legs to move with proper relative phase shiftensure equal weight distribution on all a tive legsAntigen = load on a legAntibody = movement of legB-CellOne for ea h leg
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 45 / 58
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Appli ations Walking RobotsGait A quisitionIshiguro et al. on HexapodsWalking requires legs to move with proper relative phase shiftensure equal weight distribution on all a tive legsAntigen = load on a legAntibody = movement of legB-CellOne for ea h leg
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 45 / 58
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Appli ations Walking RobotsGait A quisitionContinuedReal ve tor Shape spa eTwo thresholdslower than low threshold: leg swings ba khigher than high threshold: leg swings forwardIn betweenlift leg (ina tive leg, a tive Ab)
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 46 / 58
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Appli ations Walking RobotsGait A quisitionContinuedReal ve tor Shape spa eTwo thresholdslower than low threshold: leg swings ba khigher than high threshold: leg swings forwardIn betweenlift leg (ina tive leg, a tive Ab)
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 46 / 58
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Appli ations Walking RobotsGait A quisitionContinuedReal ve tor Shape spa eTwo thresholdslower than low threshold: leg swings ba khigher than high threshold: leg swings forwardIn betweenlift leg (ina tive leg, a tive Ab)
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 46 / 58
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Appli ations Roboti s and Emergent BehaviorOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 47 / 58
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Appli ations Roboti s and Emergent BehaviorRoboti s and Emergent BehaviorTheoryGroup of agents in an environmentDistributed Autonomous Roboti Systems (DARSs)Subtasks and a global goalEa h agent has to understand the goal of the olle tiveDe ide on their own behavior autonomouslyCommuni ate and ooperate with other agents to a omplish globalgoalClonal sele tion to develop strategiesImmune network of antibodiees to ontrol inter-agent intera tionsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 48 / 58
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Appli ations Roboti s and Emergent BehaviorRoboti s and Emergent BehaviorTheoryGroup of agents in an environmentDistributed Autonomous Roboti Systems (DARSs)Subtasks and a global goalEa h agent has to understand the goal of the olle tiveDe ide on their own behavior autonomouslyCommuni ate and ooperate with other agents to a omplish globalgoalClonal sele tion to develop strategiesImmune network of antibodiees to ontrol inter-agent intera tionsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 48 / 58
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Appli ations Roboti s and Emergent BehaviorRoboti s and Emergent BehaviorTheoryGroup of agents in an environmentDistributed Autonomous Roboti Systems (DARSs)Subtasks and a global goalEa h agent has to understand the goal of the olle tiveDe ide on their own behavior autonomouslyCommuni ate and ooperate with other agents to a omplish globalgoalClonal sele tion to develop strategiesImmune network of antibodiees to ontrol inter-agent intera tionsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 48 / 58
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Appli ations Roboti s and Emergent BehaviorRoboti s and Emergent BehaviorAnalogy to BiologyBiology DARSAntigen EnvironmentAntibody Strategy of A tionB-Cell RobotT-Cell Control ParameterStimulus Adequate behaviorSupression Inadequate BehaviorPlasma Cell Ex ellent RobotIna tivated Cell Crappy RobotAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 49 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsOutline1 Some Biology - The Human Immune SystemThe Two-Tier DefenseClonal Sele tionNegative Sele tionPositive Sele tionSelf/Non-Self Dis riminationImmune Network Theory2 Why the Immune SystemWhy the Immune System3 Framework for Engineering AISThe TheoryAn Example4 Appli ationsComputer Se urityWalking RobotsRoboti s and Emergent BehaviorEmergent AIS for Autonomous Mobile RobotsAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 50 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsProblem Des riptionGroup of autonomous agentsNavigate to destinationAvoid obsta les
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 51 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISAntigenSensor informationdire tion + distan e to obsta les and destinationbinary stringsparatope = pre onditionidiotope = disallowed ondition
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 52 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISAntigenSensor informationdire tion + distan e to obsta les and destinationbinary stringsparatope = pre onditionidiotope = disallowed ondition
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 52 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISAntigenSensor informationdire tion + distan e to obsta les and destinationbinary stringsparatope = pre onditionidiotope = disallowed ondition
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 52 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISAntigenSensor informationdire tion + distan e to obsta les and destinationbinary stringsparatope = pre onditionidiotope = disallowed ondition
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 52 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISAntibodyAntibody = a tion for antigen
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 53 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISMultiple AntibodiesAntigen Epitope AntibodyC1 Ab1C2 Ab2No intera tion between Ab1 and Ab2Same in rease in on entration
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 54 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISMultiple AntibodiesAntigen Epitope AntibodyC1 Ab1C2 Ab2No intera tion between Ab1 and Ab2Same in rease in on entration
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 54 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISInteresting CaseIdiotype of Ab1 == paratope of Ab2Ab2 is stimulated by Ab1But Ab1 is suppressed by Ab2Result: on entration(Ab2) > on entration(Ab1)
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 55 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISInteresting CaseIdiotype of Ab1 == paratope of Ab2Ab2 is stimulated by Ab1But Ab1 is suppressed by Ab2Result: on entration(Ab2) > on entration(Ab1)
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 55 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISInteresting CaseIdiotype of Ab1 == paratope of Ab2Ab2 is stimulated by Ab1But Ab1 is suppressed by Ab2Result: on entration(Ab2) > on entration(Ab1)
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 55 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISInteresting CaseIdiotype of Ab1 == paratope of Ab2Ab2 is stimulated by Ab1But Ab1 is suppressed by Ab2Result: on entration(Ab2) > on entration(Ab1)
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 55 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsAISPrioritiesAb2 is more likely�given higher priority�Antibodies are priority adjustment me hanisms
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 56 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsDynami sDegree of intera tion between two antibodiesDegree of intera tion between antibody and antigen
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 57 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsDynami sDegree of intera tion between two antibodiesDegree of intera tion between antibody and antigen
Ashwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 57 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsDynami sInnovationAdaptation for adjustment: mutate bitstringsBut we're more interested in adaptation for innovation:De aying positive sele tion for removalSele tion (simulations work better with this):m random antibodies by gene re ombinationAllow into available repertoire based onself-a�nitysensitivityAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 58 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsDynami sInnovationAdaptation for adjustment: mutate bitstringsBut we're more interested in adaptation for innovation:De aying positive sele tion for removalSele tion (simulations work better with this):m random antibodies by gene re ombinationAllow into available repertoire based onself-a�nitysensitivityAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 58 / 58
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Appli ations Emergent AIS for Autonomous Mobile RobotsDynami sInnovationAdaptation for adjustment: mutate bitstringsBut we're more interested in adaptation for innovation:De aying positive sele tion for removalSele tion (simulations work better with this):m random antibodies by gene re ombinationAllow into available repertoire based onself-a�nitysensitivityAshwin Pan hapakesan (OCICS) Arti� ial Immune Systems COMP5206 / CSI5183 58 / 58
