2. TRU-COMP3710 Artificial Life and Emergent Behavior1 Course Outline Part I – Introduction to Artificial Intelligence Part II – Classical Artificial Intelligence Part III – Machine Learning Introduction to Machine Learning Neural Networks Probabilistic Reasoning and Bayesian Belief Networks Artificial Life: Learning through Emergent Behavior Part IV – Advanced Topics

3. Artificial Life and Emergent Behavior Fall 2013 Comp3710 Artificial Intelligence Computing Science Thompson Rivers University

4. Emergent Behavior3 Reference Artificial Intelligence Illuminated, Ben Coppin, Jones and Bartlett Illuminated Series Artificial Life – http://www.alesdar.org/oldSite/IS/http://www.alesdar.org/oldSite/IS/ …

5. Emergent Behavior4 Chapter Contents 1. What is life? What is life? 2. Emergent Behavior Emergent Behavior 3. Finite State Automata Finite State Automata 4. Conway’s Life Conway’s Life 5. One-Dimensional Cellular Automata One-Dimensional Cellular Automata 6. Self-Reproducing Systems Self-Reproducing Systems 7. Evolution and Evolution Strategies Evolution and Evolution Strategies 8. Genetic Programming Genetic Programming 9. Artificial Immune Systems Artificial Immune Systems

6. Emergent Behavior5 1. What is life? What are the defining features of life? self-reproduction ability to evolve by Darwinian natural selection response to stimuli ability to die growth or expansion Not all living things obey these rules E.g., mules Some things that are not alive do E.g., viruses, computer viruses Defining life is very difficult!

7. Emergent Behavior6 2. Emergent Behavior Emergent behavior is a prominent feature of life. The idea that complex behavior emerges from simple rules. For Goldstein, emergence can be defined as: "the arising of novel and coherent structures, patterns and properties during the process of self- organization in complex systems" (Corning 2002).Corning 2002 Photo taken and supplied by Brian Voon Yee Yap. Cathedral Termite Mounds.

8. Emergent Behavior7 Examples Boids – Boids Craig Reynolds in 1987 Simulations of birds given very simple rules about how to fly. Automatically flew in such a way as to avoid large obstacles, without being taught explicitly how to do so. Rules Tendency to move toward the center of gravity of the whole flock No collision each other No other rule except the above two Now to be used in animation software eFloys (evolving Floyes) eFloys Social, territorial, evolving artificial life creatures Rules Territorialism (they defend their territory against intruders) Potential individualism (each can possess a different personality) Ability to evolve (using a Genetic Algorithm code).

9. Emergent Behavior8 3. Finite State Automata Very useful tool for AI, and computer science in general FSA: a machine with a finite number of states. A state represents a set of internal data at a certain moment. The FSA is given inputs, which result in transitions between states. Some states are accepting, meaning the FSA is saying “Yes”. Other states are rejecting. In this example there are two possible input characters – a and b, and two states, 1 and 2. Meaning of the FSA is ??? It will finish in state 1 (the accepting state) if the input has an even number of a’s.

10. Emergent Behavior9 Finite State Automata Very useful tool for AI, and computer science in general It is possible to mimic certain behaviors of living creatures using FSAs. E.g., Boids Inputs – location, speed, other birds, obstacles, … State – direction, speed A set of rules that determine which state to move to from each state, according to the input data Can you make a FSA for variable names in Java? A variable consists of characters, numbers and ‘_’, Must not start with a number Can you make a FSA for pendulous words, e.g., abcddcba?

11. Emergent Behavior10 4. Cellular automata

12. Emergent Behavior11 Conway’s Life A two dimensional cellular automaton. A two dimensional grid of cells, each of which can be alive or dead. A set of rules determines how the cells will change from one generation to the next: 1. A dead cell will come to life if it has three living neighbors. 2. A living cell with two or three living neighbors, will stay alive. 3. A living cell with fewer than two living neighbors will die of loneliness. 4. A living cell with more than three living neighbors will die of overcrowding.

13. Emergent Behavior12 Conway’s Life Surprisingly complex behavior can sometimes emerge from these simple rules. This diagram shows a successive sequence of generations of Conway’s Life. This pattern is known as a glider. There is also a pattern known as a glider gun which constantly fires out gliders.

14. Emergent Behavior13 5. One-Dimensional Cellular Automata A single line of cells. Each cell thus has two immediate neighbors. It is usual to have rules that take into account the cells on either side of the immediate neighbors as well. Usually, the cell itself is also taken into account, meaning that each cell’s future is determined by 5 cells. 1-D Cellular Automata often use totalistic rules, meaning that the number of living cells out of the 5 is all that determines the cell’s state in the next generation. At least two living neighbors, then it will live Less than two neighbors, then it will stay dead If a dead cell has at least three living neighbors, then it will come to life

15. Emergent Behavior14 One-Dimensional Cellular Automata Hence, there are 32 possible rule sets. One such set of rules might be: 1 2345 0 0111 This says a cell can only survive if it has two, three or four neighbors. This rule can be seen applied in five generations in the following diagram: Applications: image processing 1 st generation 2 nd 3 rd 4 th 5 th

16. Emergent Behavior15 More demonstration Game of Life Animal patterns

17. Emergent Behavior16 6. Self-Reproducing Systems Von Neumann proposed a self-reproducing system based on cellular automata in the 1950s. Langton invented loops:loops 8 different states Contains all the information that is needed to reproduce itself. The artificial system that was capable of self-reproducing. The “organism” that Langton created lived in an environment that was nothing more than a board of cells; each cell contains its own state. Depending upon the state of a cell and the state of the cells around it, a cell’s state might change in the next generation. The creature that Langton created in this environment looked like a loop with a tail. As time advanced the tail extended and made a new loop. This new loop then severed itself from the original loop organism and then there were two organisms. Both the new creature and the original creature were able to reproduce, and so they did, eventually filling up all the available space [Levy, 1992].

18. Emergent Behavior17 Self-Reproducing Systems HAL 9000 in the movie “2001: A Space Odyssey” was inspired by Langton’s loops Ultimately we may have robots that can obtain the raw materials necessary to produce new versions of themselves. This would be useful for exploring other planets.

19. Emergent Behavior18 7. Evolution The changes in cellular automata involve single-step selection, while evolutionary systems involve cumulative selection (Dawkins, 1991). Survival of the fittest means that creatures that are fit are more likely to reproduce than those that are less fit. This idea is modeled exactly in systems such as genetic algorithms. Dawkins’ biomorphs Nine genes representing a feature of the biomorphs, such as the branching angle between branches or the length of branches. Evolved Virtual Creatures Karl Sims in 1994 Darwinian evolutions of virtual block creatures

20. Emergent Behavior19 Evolution Strategies Similar to hill-climbing. A set of numeric parameters is varied from generation to generation by making normally distributed changes to the values. If the offspring is a better solution than the parent, then the process repeats from the offspring. Otherwise, the offspring is rejected, and a new attempt is made. This is asexual reproduction – a single parent produces a single offspring.

21. Emergent Behavior20 8. Genetic Programming http://www.genetic-programming.org/ http://www.genetic-programming.com/gpanimatedtutorial.html Genetic programming (GP) is an automated method for creating a working computer program from a high-level problem statement of a problem. Genetic programming starts from a high-level statement of “what needs to be done” and automatically creates a computer program to solve the problem. Not genetic algorithms U. S. PATENT 5,719,794

22. Emergent Behavior21 9. Artificial Immune Systems Systems modeled on the immune systems in humans and other biological creatures. Used in anti-virus systems, for example. Also applied in computer security, for solving combinatorial problems, and for machine learning problems.