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Ai presentation

  1. 1. ARTIFICIAL INTELLIGENCE
  2. 2. INTRODUCTION <ul><li>W.W.W. – a virtual society. </li></ul><ul><li>WEB MINING – a challenging activity. </li></ul><ul><li>Web – unstructured data, changes are frequent and rapid. </li></ul>
  3. 3. WEB MINING <ul><li>Process of discovering useful information or knowledge from hyperlink structure, page contents and data usage. </li></ul><ul><li>Three types: </li></ul><ul><li>Web structure mining. </li></ul><ul><li>Web content mining. </li></ul><ul><li>Web usage mining. </li></ul>
  4. 4. WEB STRUCTURE MINING <ul><li>Discovering knowledge from hyperlinks. </li></ul><ul><li>Pages are ranked according to prestige. </li></ul><ul><li>Pages are social animals and hyperlinks are relations. </li></ul>
  5. 5. WEB CONTENT MINING <ul><li>Extracts useful information from the content of web pages. </li></ul><ul><li>Pages are classified according to their topics, or user’s option. </li></ul><ul><li>Ex. -- <meta> tag used in html scripting. </li></ul>
  6. 6. WEB USAGE MINING <ul><li>Aims to automatically discover and analyze patterns. </li></ul><ul><li>Profiles of users interacting with a web site is captured, modeled and analyzed in order to improve services. </li></ul><ul><li>Ex. -- private messaging in “orkut” </li></ul>
  7. 7. SWARM INTELLIGENCE
  8. 8. SWARM INTELLIGENCE <ul><li>Used in representing collective behavior of decentralized, self organized artificial systems. </li></ul><ul><li>Collective behavior can lead to emergence of apparent intelligent behavior. </li></ul><ul><li>SI systems are able to communicate with each other and to interact with their environment. </li></ul>
  9. 9. EXAMPLES OF SI SYSTEM <ul><li>Bird flocking </li></ul><ul><li>Ant colony </li></ul><ul><li>Animal herding </li></ul>
  10. 10. ANT COLONY OPTIMIZER <ul><li>Inspired by ants. </li></ul><ul><li>Ants find path from nest to food by creating a network of pheromone trails. </li></ul><ul><li>Similar to ants a www user navigates without the information of route and without having a view of global environment. </li></ul>
  11. 11. BIOLOGICAL INSPIRATION <ul><li>A French entomologist observed that: </li></ul><ul><li>A significant stimulus is an indirect, non symbolic form of communication, mediated by the environment. </li></ul><ul><li>Information is local, can be accessed only by visiting local area in which it was released. </li></ul>
  12. 12. Swarming – The Definition <ul><li>aggregation of similar animals, generally cruising in the same direction </li></ul><ul><li>Termites swarm to build colonies </li></ul><ul><li>Birds swarm to find food </li></ul><ul><li>Bees swarm to reproduce </li></ul>
  13. 13. Why do animals swarm? <ul><li>To forage better </li></ul><ul><li>To migrate </li></ul><ul><li>As a defense against predators </li></ul><ul><li>Social Insects have survived for millions of years. </li></ul>
  14. 14. Swarming is Powerful <ul><li>Swarms can achieve things that an individual cannot </li></ul>
  15. 15. Collision Avoidance <ul><li>Rule 1: Avoid Collision with neighboring birds </li></ul>
  16. 16. Velocity Matching <ul><li>Rule 2: Match the velocity of neighboring birds </li></ul>
  17. 17. Flock Centering <ul><li>Rule 3: Stay near neighboring birds </li></ul>
  18. 18. Swarming - Characteristics <ul><li>Simple rules for each individual </li></ul><ul><li>No central control </li></ul><ul><ul><li>Decentralized and hence robust </li></ul></ul><ul><li>Emergent </li></ul><ul><ul><li>Performs complex functions </li></ul></ul>
  19. 19. Learn from insects <ul><li>Computer Systems are getting complicated </li></ul><ul><li>Hard to have a master control </li></ul><ul><li>Swarm intelligence systems are: </li></ul><ul><ul><li>Robust </li></ul></ul><ul><ul><li>Relatively simple </li></ul></ul>
  20. 20. Applications <ul><li>Movie effects </li></ul><ul><ul><li>Lord of the Rings </li></ul></ul><ul><li>Network Routing </li></ul><ul><ul><li>ACO Routing </li></ul></ul><ul><li>Swarm Robotics </li></ul><ul><ul><li>Swarm bots </li></ul></ul>
  21. 21. Particle Swarm Optimization
  22. 22. Particle Swarm Optimization <ul><li>Particle swarm optimization imitates human or insects social behavior. </li></ul><ul><li>Individuals interact with one another while learning from their own experience, and gradually move towards the goal. </li></ul><ul><li>It is easily implemented and has proven both very effective and quick when applied to a diverse set of optimization problems. </li></ul>
  23. 23. <ul><li>Bird flocking is one of the best example of PSO in nature. </li></ul><ul><li>One motive of the development of PSO was to model human social behavior. </li></ul>
  24. 24. Applications of PSO <ul><li>Neural networks like Human tumor analysis, Computer numerically controlled milling optimization; </li></ul><ul><li>Ingredient mix optimization; </li></ul><ul><li>Pressure vessel (design a container of compressed air, with many constraints). </li></ul><ul><li>Basically all the above applications fall in a category of finding the global maxima of a continuous, discrete, or mixed search space, with multiple local maxima. </li></ul>
  25. 25. Algorithm of PSO <ul><li>Each particle (or agent) evaluates the function to maximize at each point it visits in spaces. </li></ul><ul><li>Each agent remembers the best value of the function found so far by it (pbest) and its co-ordinates. </li></ul><ul><li>Secondly, each agent know the globally best position that one member of the flock had found, and its value (gbest). </li></ul>
  26. 26. Algorithm – Phase 1 (1D) <ul><li>Using the co-ordinates of pbest and gbest, each agent calculates its new velocity as: </li></ul><ul><li>v i = v i + c 1 x rand() x (pbestx i – presentx i ) </li></ul><ul><li>+ c 2 x rand() x (gbestx – presentx i ) </li></ul><ul><li>where 0 < rand() <1 </li></ul><ul><li>presentx i = presentx i + (v i x Δ t) </li></ul>
  27. 27. Algorithm – Phase 2 (n-dimensions) <ul><li>In n-dimensional space : </li></ul>
  28. 28.
  29. 29. Ant Colony Optimization
  30. 30. Ant Colony Optimization - Biological Inspiration <ul><li>Inspired by foraging behavior of ants. </li></ul><ul><li>Ants find shortest path to food source from nest. </li></ul><ul><li>Ants deposit pheromone along traveled path which is used by other ants to follow the trail. </li></ul><ul><li>This kind of indirect communication via the local environment is called stigmergy. </li></ul><ul><li>Has adaptability, robustness and redundancy. </li></ul>
  31. 31. Foraging behavior of Ants <ul><li>2 ants start with equal probability of going on either path. </li></ul>
  32. 32. Foraging behavior of Ants <ul><li>The ant on shorter path has a shorter to-and-fro time from it’s nest to the food. </li></ul>
  33. 33. Foraging behavior of Ants <ul><li>The density of pheromone on the shorter path is higher because of 2 passes by the ant (as compared to 1 by the other). </li></ul>
  34. 34. Foraging behavior of Ants <ul><li>The next ant takes the shorter route. </li></ul>
  35. 35. Foraging behavior of Ants <ul><li>Over many iterations, more ants begin using the path with higher pheromone, thereby further reinforcing it. </li></ul>
  36. 36. Foraging behavior of Ants <ul><li>After some time, the shorter path is almost exclusively used. </li></ul>
  37. 37. Various Algorithms <ul><ul><li>First ACO algorithm. </li></ul></ul><ul><ul><li>Pheromone updated by all ants in the iteration. </li></ul></ul><ul><ul><li>Ants select next vertex by a stochastic function which depends on both pheromone and problem-specific heuristic </li></ul></ul>
  38. 38. Various Algorithms - 2 <ul><li>Ant Colony System (ACS): </li></ul><ul><ul><li>Local pheromone update in addition to offline pheromone update. </li></ul></ul><ul><ul><li>By all ants after each construction step only to last edge traversed. </li></ul></ul><ul><ul><li>Diversify search by subsequent ants and produce different solutions in an iteration. </li></ul></ul><ul><ul><li>Local update: </li></ul></ul><ul><ul><li>Offline update : </li></ul></ul>
  39. 39. Theoretical Details <ul><li>Convergence to optimal solutions has been proved. </li></ul><ul><li>Can’t predict how quickly optimal results will be found. </li></ul><ul><li>Suffer from stagnation and selection bias. </li></ul>
  40. 40. ACO in Network Routing
  41. 41. Ant like agents for routing <ul><li>Intuitive to think of ants for routing. </li></ul><ul><li>Aim is to get shortest path </li></ul><ul><li>Start as usual </li></ul><ul><ul><li>Release a number of ants from source, let the age of ant increases with increase in hops </li></ul></ul><ul><ul><li>decide on pheromone trails . </li></ul></ul><ul><li>Problem – Ants at an node do not know the path to destination, can't cahnge table entry </li></ul>
  42. 42. Routing continued ... <ul><li>Possible Solutions </li></ul><ul><ul><li>first get to dest. and then retrace </li></ul></ul><ul><ul><ul><li>Needs memory to store the path </li></ul></ul></ul><ul><ul><ul><li>And intelligence to revert the path </li></ul></ul></ul><ul><ul><li>Leave unique entries on nodes </li></ul></ul><ul><ul><ul><li>a lot of entries at every node </li></ul></ul></ul><ul><li>Observation – At any intermediate node, ant knows the path to source from that node. </li></ul><ul><ul><li>now leave influence on routing table having entry “route to source via that link” </li></ul></ul>
  43. 43. Routing contd ... <ul><li>Now at any node it has information about shortest path to dest., left by ants from dest. </li></ul><ul><li>The ant following shortest path should have maximum influence </li></ul><ul><li>A convenient form of pheromone can be inverse of age + constant </li></ul><ul><li>The table may get frozen, with one entry almost 1, add some noise f i.e probabilty that an ant choses purely random path </li></ul>
  44. 44. Dealing with congestion <ul><li>Add a function of degree of congestion of each node to age of an ant </li></ul><ul><li>Delay an ant at congested node, this prevents ants from influencing route table </li></ul>
  45. 45. SI - Limitations <ul><li>Theoretical analysis is difficult, due to sequences of probabilistic choices </li></ul><ul><li>Most of the research are experimental </li></ul><ul><li>Though convergence in guaranteed, time to convergence is uncertain </li></ul>
  46. 46. Conclusion <ul><li>Provide heuristic to solve difficult problems </li></ul><ul><li>Has been applied to wide variety of applications </li></ul><ul><li>Can be used in dynamic applications </li></ul>

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