Presentation By SANJOG BHATTA Student ID : July 1’ 2009

Presentation Organization What is Immune System? Features of Immune system Introduction to Artificial Immune System (AIS) IS inspired Algorithms What are its major Applications? Connection between our lab and AIS

Immune System One of the major systems of organism Integrated biological processes within an organism Protects against diseases, neutralize pathogens, tumor cells (Defense System) Provides Layered Protection Keeps memories of past encounters Maintain the proper body functionality Effect can be seen when some organism dies

Features Self – recognition Self – organization Feature Extraction Learning & Memory Adaptability Ability to ‘forget’ little-used information Decentralized control

Role of Immune System It protects our bodies from infection, operating via: - A first line nonspecific line of defence: barriers - A second nonspecific line of defence: general attack. Then comes specific (i.e. targeted) defence, comprising: Primary immune response Launches a response to invading pathogens Secondary immune response Remembers past encounters, leading to: Faster response the second time around

Types of Responses/Systems Inflammatory Response (Innate) Non-Specific Response Immune Response (Adaptive) Specific Response Memory Capabilities Animation

Layers of Immune System

In order to Respond to Pathogens, but to avoid responding to and destroying cells from its own body, Lymphocytes (WBC) MUST BE ABLE TO RECOGNIZE A PATHOGEN AS A FOREIGN INVADER AND DISTINGUISH IT FROM CELLS OF THE BODY This is the key to it all, and where most of the inspiration comes for computational systems

Artificial Immune System (AIS) “Artificial immune systems are intelligent methodologies inspired by the immune system toward real-world problem solving.” (Dasgupta, 1999) "Artificial immune systems (AIS) are adaptive systems, inspired by theoretical immunology and observed immune functions, principles and models, which are applied to problem solving.” (de Castro, Timmis, 2002)

Principles of AIS Robustness: Consequence of the fact that IS is diverse, distributed, dynamic and error tolerant. Adaptability: Can learn to recognize and respond to new infections and retain a memory of them. Autonomy: No outside/single control required. Anomaly Detection: Ability to detect novel pathogens to which it has not been previously exposed. Diversity, Generality: Unique IS for each individual in a population Distributed Protection: Millions of distributed components that interact locally to provide protection. Reinforcement learning and Memory: The system can “learn” the structures of pathogens, so that future responses to the same pathogens are faster and stronger.

General Framework Application Domain Representation Affinity Measures Immune Algorithms Solution Representation: To create abstract models of immune organs, cells and molecules Affinity Measures: To quantify the interactions of these elements Immune Algorithms: Govern the dynamics of the AIS Shape space, Binding Interaction, Distance

Basic Immune Algorithms Bone Marrow Models Negative Selection Algorithms Clonal Selection Algorithm Somatic Hypermutation Immune Network Models

Bone Marrow Models Gene libraries are used to create antibodies from the bone marrow Use this idea to generate attribute strings that represent receptors Antibody production through a random concatenation from gene libraries

Negative Selection Algorithm Forrest 1994: Idea taken from the negative selection of T-cells in the thymus Applied initially to computer security Split into two parts: Censoring Monitoring Detector Generation Monitoring Phase

Clonal Selection Algorithm De Castro & Von Zuben (2000) Randomly initialise a population (P) For each pattern in Ag Determine affinity to each Ab in P Select n highest affinity from P Clone and mutate prop. to affinity with Ag Add new mutants to P endFor Select highest affinity Ab in P to form part of M Replace n number of random new ones Until stopping criteria

Somatic Hypermutation Mutation rate in proportion to affinity Very controlled mutation in the natural immune system The greater the antibody affinity the smaller its mutation rate Classic trade-off between exploration and exploitation α – Mutation Rate D* - Affinity

Immune Network Models Temmis & Neel 2000 Initialise the immune network (P) For each pattern in Ag Determine affinity to each Ab in P Calculate network interaction Allocate resources to the strongest members of P Remove weakest Ab in P EndFor If termination condition met exit else Clone and mutate each Ab in P (based on a given probability) Integrate new mutants into P based on affinity Repeat

Applications Security and Virus Detection Robotics Control Optimization Neural Network Approaches Anomaly Detection Agent Based Approaches Learning Inductive Problem Solving Pattern Recognition Computer Models Data Mining

Control Clonal Selection & Affinity Maturation Identification, Synthesis and Adaptive Control Sequential Control Immune ResponseIntelligent Control Antigen is present in the systemDisturbance is present in the system Innate immunity is the first line of defenseRobust feedback control (non-adaptive) Ag presentation (by macrophages) cause T- cell response (adaptation) A utility function translates the disturbances into an error function and presents it to the critic which starts the adaptation process T-cells activate B-cell response (adaptation) to counteract antigen The adaptive critics modify the controller parameters to counteract disturbances For certain types of Ag, the B-cells are triggered without the help of T-cells but still with the help of some APC’s For certain classes of disturbances, the controller is adapted directly based on a utility measure

Robotics Autonomous Robot Navigation Behavior Arbitration Emergence of Collective Behavior DARSImmune System EnvironmentAntigen Strategy of ActionAntibody RobotB-cell Control ParameterT-cell AdequateStimulus InadequateSuppression Excellent RobotPlasma cell Distributed Autonomous Robotic Systems (DARS) Individually understand the objective of the system, environment, the behavior of other robots Decide their own behavior No central control The clonal selection algorithm was used for transmitting high quality strategies among robots, while the immune network of antibodies controlled the interactions among individual robots and the B-T-cell interactions regulated the adaptability of the agents to the changing environment

Learning Immune Memory Concept of Mutation Pattern Recognition Somatic Hypermutation – continuous RL Clonal Selection Algorithm