biologically-inspired computing lecture 20 Informatics luis rocha 2015 biologically Inspired computing INDIANA UNIVERSITY

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1 lecture 20 -inspired

2 Sections I485/H400 course outlook Assignments: 35% Students will complete 4/5 assignments based on algorithms presented in class Lab meets in I1 (West) 109 on Lab Wednesdays Lab 0 : January 14 th (completed) Introduction to Python (No Assignment) Lab 1 : January 28 th Measuring Information (Assignment 1) Graded Lab 2 : February 11 th L-Systems (Assignment 2) Graded Lab 3: March 25 th Cellular Automata & Boolean Networks (Assignment 3) Graded Lab 4: April 8 th Genetic Algorithms (Assignment 4) Due: April 22 nd Lab 5: April 22 nd Ant Clustering Algorithm (Assignment 5) Due May 4 th

3 Readings until now Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapters 1,2,3,7,8 Chapter 5, all sections Section 7.7, 8.3.1,8.3.6, Lecture notes Chapter 1: What is Life? Chapter 2: The Logical Mechanisms of Life Chapter 3: Formalizing and Modeling the World Chapter 4: Self-Organization and Emergent Complex Behavior Chapter 5: Reality is Stranger than Fiction posted

4 final project schedule ALIFE 15 Projects Due by May 4 th in Oncourse ALIFE 15 (14) Actual conference due date: pages (LNCS proceedings format) D= Preliminary ideas overdue! Individual or group With very definite tasks assigned per member of group

5 Flocking Behavior Boids by Craig Reynolds (1986) 3 Steering behaviors Alignment: move towards the average heading of local flockmates Adjust velocity direction according to others in vicinity Separation: steer to avoid crowding local flockmates Maintain minimum distance to others (adjusting speed) Cohesion: steer to move toward the average position of local flockmates Adjust velocity direction according to others in vicinity Each boid sees only flockmates within a certain small neighborhood around itself.

6 Separation: maintain minimum distance adjusting speed Boid rules Figures by Rudy Rucker in Artificial Life Lab.

7 velocity vector update Boid rules Alignment: steer towards the average heading of local flockmates Cohesion: steer to move toward the average position of local flockmates Figure by Rudy Rucker in Artificial Life Lab.

8 Flocking Behavior Boids by Craig Reynolds (1986) 3 Steering behaviors Separation: steer to avoid crowding local flockmates Maintain minimum distance to others (speed adjustment) Alignment: move towards the average heading of local flockmates Adjust velocity according to others in vicinity Cohesion: steer to move toward the average position of local flockmates

9 Batman Returns to simulate bats and penquins Cliffhanger Simulation of bats Jurassic Park Simulation of gallamunus herd The Lion King Scene of wildbeast stampede Jumanji Stampede of zoo animals Star Trek Voyager "Elogium Simulation a swarm of space creatures Boids Used in Movies

10 based on boids flocking robots Cybernetic Intelligence Research Group, University of Reading, England Intelligent Autonomous Systems Laboratory. University of the West of England.

11 social flocking Search by flocking Social flocks looking for good positions Metaphor: food, resources Agents flock according to social knowledge of Their best position so far The best position of the swarm or local neighbors Not necessarily neighbors in search space but in some social structure (e.g. one dimensional lattice) Algorithm Generate a random population of particles x i (t) --- vector of variables (similar to genotypes) The position of agent i is x i moving with velocity vector v i x i (t+1) = x i (t) + v i (t + 1) Velocity update rule w : inertial constant c 1 and c 2 : constants r 1 and r 2 : random values in [0,1] particle swarm optimization (PSO) xˆ x( t) c. r xˆ ( ) v( t 1) w. v( t) c. r1 2 2 s x 1 t Agent best so far (cognitive term) x Swarm best (social term)

12 The workings particle swarm optimization (PSO) 1. Generate random population of particles in search space 2. Generate random velocity vectors for each particle 3. Repeat (t++) For every particle i in population If f x i t f ˆx i then xˆ i x i t Compute xˆ s v( t 1) w. v( t) c. r ˆ ( ). ˆ 1 x x t c2 r2 xs x( x( t 1) x( t) v( t 1) 4. Until maximum iterations ) 1 t

13 dumb agents, intelligent collective swarm intelligence Bio-inspired methodology for solving distributed problems biological examples social insects ants, termites, bees, wasps swarming, flocking, herding behaviors in vertebrates. Collective behavior algorithms Distributed or decentralized control No central control or agent Local communication among agents Self-organization Simple agents, complicated emergent behavior Robust To individual loss Adaptive and Flexible Capability to respond to perturbations

14 antz Z-4195: Why'd I have to be born a worker? You soldiers get all the glory. Plus, you get to go out in the world. You know, you meet interesting insects; you get to kill them. Weaver: Yeah, but you get to spend all day with those beautiful worker girls. Z-4195: Weaver, they're CAREER girls. They're obsessed with digging. Z-4195 I'm supposed to do everything for the colony? What about my needs?

15 stigma + ergon = mark + work Process of communication by changing environment Pheromone trails Nest Building Termites use a simple rule: Each agent scoops up a 'mudball' and covers it with pheromones Others are attracted by pheromone and are therefore more likely to drop their own mudballs near their neighbors Introduced by Pierre-Paul Grassé in 1959 "Stimulation of workers by the performance they have achieved. Regulation of behavior (and constructions) is dependent on the behavior of others and the environment they build Worker is guided by work Used in optimization algorithms Stigmergy: Ant colony algorithms Flocking behavior: Particle Swarm Optimization stigmergy

16 natural achievements termite mounds

17 natural architecture From Guy Theraulaz wasp nests Typical tasks for social insects: find appropriate place to build nest, build and maintain nest, task allocation,feed colony, find food, respond to challenges, send an alarm, etc.

18 Aimless bots termites Very simple Agents that primarily wander around randomly Mitchell Resnick Rules Wander aimlessly until bumping into a wood chip (Random walk) If carrying a wood chip, drop it and wander Else, pick up chip and wander Figure by Gary Flake in The Computational Beauty of Nature.

19 Probabilistic cleaning Very simple rules for colony clean up Pick dead ant. if a dead ant is found pick it up (with probability inversely proportional to the quantity of dead ants in vicinity) and wander. Drop dead ant. If dead ants are found, drop ant (with probability proportional to the quantity of dead ants in vicinity) and wander. ants Figure by Marco Dorigo in Real ants inspire ant algorithms See Also: J. L. Deneubourg, S. Goss, N. Franks, A. Sendova-Franks, C. Detrain, L. Chretien. The Dynamics of Collective Sorting Robot-Like Ants and Ant-Like Robots. From Animals to Animats: Proc. of the 1st Int. Conf. on Simulation of Adaptive Behaviour (1990).

20 Clustering by collective or swarm robots ant-inspired robots Becker et al Rules Move: with no sensor activated move in straight line Obstacle avoidance: if obstacle is found, turn with a random angle to avoid it and move. Pick up and drop: Robots can pick up a number of objects (up to 3) If shovel contains 3 or more objects, sensor is activated and objects are dropped. Robot backs up, chooses new angle and moves. Results in clustering The probability of dropping items increases with quantity of items in vicinity Figure from R Beckers, OE Holland, and JL Deneubourg [1994]. From local actions to global tasks: Stigmergy and collective robotics. In Artificial Life IV.

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24 S Goss, S Aron, JL Deneubourg, JM Pasteels [1989]. Self-organized shortcuts in the Argentine ant. Naturwissenschaften, 76:

25 stigmergy at work: ant colony optimization foraging, routing, and optimization J.L. Deneubourg, S. Aron, S. Goss, J.M. Pasteels [1990 ] The selforganizing exploratory pattern of the argentine ant. Journal of Insect Behavior. After an initial transitory phase lasting few minutes during which some oscillations can appear, ants tend to converge on the same path

26 stigmergy at work S Goss, S Aron, JL Deneubourg, JM Pasteels [1989]. Self-organized shortcuts in the Argentine ant. Naturwissenschaften, 76, pp foraging, routing, and optimization

27 based on dead body cleaning ant clustering algorithm (ACA) Very simple rules for colony clean up Pick dead ant. if a dead ant is found pick it up (with probability inversely proportional to the quantity of dead ants in vicinity) and wander. Drop dead ant. If dead ants are found, drop ant (with probability proportional to the quantity of dead ants in vicinity) and wander. Data vector: X x 1 x 2 x 3 x n-1 x n x 1 x 2 x 3 x n-1 x n Lumer, E. D. and Faieta, B Diversity and adaptation in populations of clustering ants. In From Animals To Animats 3, pp x 1 x 2 x 3 x n-1 x n Cluster data (N samples) according to ant clean up rules

28 readings Next lectures Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 5, all sections Section 7.7, 8.3.1,8.3.6, Lecture notes Chapter 1: What is Life? Chapter 2: The logical Mechanisms of Life Chapter 3: Formalizing and Modeling the World Chapter 4: Self-Organization and Emergent Complex Behavior Chapter 5: Reality is Stranger than Fiction posted Optional materials Scientific American: Special Issue on the evolution of Evolution, January 2009.