Robotics Versus Artificial Intelligence. Search. SearchSearch “All AI is search” “All AI is search”  Game theory  Problem spaces Every problem is a.

Presentation on theme: "Robotics Versus Artificial Intelligence. Search. SearchSearch “All AI is search” “All AI is search”  Game theory  Problem spaces Every problem is a."— Presentation transcript:

Robotics Versus Artificial Intelligence. Search

SearchSearch “All AI is search” “All AI is search”  Game theory  Problem spaces Every problem is a feature space of all possible (successful or unsuccessful) solutions. Every problem is a feature space of all possible (successful or unsuccessful) solutions. The trick is to find an efficient search strategy in this space. The trick is to find an efficient search strategy in this space.

Example of an Intelligent Action Getting ready to come to class Getting ready to come to class Describe so a machine could do it Describe so a machine could do it  search among alternatives (car or bus)  represent the knowledge This requires a lot of knowledge…!

SearchSearch Example: Four three-letter crossword puzzle Example: Four three-letter crossword puzzle Search problem is find correct puzzle Search problem is find correct puzzle Approaches: Approaches:  word fill:  take word,  put it to space,  if contradiction, backtrack  space fill:  take vertical or horizontal space  select a word with this length  put it into space,  if contradiction, backtrack  Many other strategies: homework, find space and operators in it, discuss backtracking strategy Cat dog cam may mom sit mit C A M A O T O M This is a “mapping” problem – belongs to CSP family of problems

Blind Search Search depends only on node’s position in the search tree Search depends only on node’s position in the search tree Two basic blind searches: Two basic blind searches:  depth-first  breadth-first Problem: define depth- first search for the above problem Problem: define depth- first search for the above problem Problem: define breadth-first algorithm for the above problem. Problem: define breadth-first algorithm for the above problem. Repeat both for each of the approaches outlined above in the previous slide. Repeat both for each of the approaches outlined above in the previous slide. Called also search strategies General problem solving method

Depth-First Search Pseudo-Code 1. Set L to list of initial nodes 1. Set L to list of initial nodes 2. n = head(L), Empty(L) ==> fail 2. n = head(L), Empty(L) ==> fail 3. If n=goal, stop, return it and return the path leading to it 3. If n=goal, stop, return it and return the path leading to it 4. pop(L), push(L) all n’s children, 4. pop(L), push(L) all n’s children, 5. go to step 2 5. go to step 2 Head = car in LISP empty = null in LISP push(L) pop(L) Depth First is based on a stack, L

Breadth-First Search 1. Set L to list of initial nodes 1. Set L to list of initial nodes 2. n=head(L), Empty(L)==> fail 2. n=head(L), Empty(L)==> fail 3. If n=goal then stop and return it and path 3. If n=goal then stop and return it and path 4. Dequeue(L), Enqueue(L) all n’s children 4. Dequeue(L), Enqueue(L) all n’s children 5. Go to step 2 5. Go to step 2 Depth First is based on a queue, L Means, remove from queue Means, add to queue

Heuristic Search Meta-level reasoning Meta-level reasoning heuristic function aids in selecting which part of search tree to expand heuristic function aids in selecting which part of search tree to expand trade-off between time to compute heuristic function and to expand the tree trade-off between time to compute heuristic function and to expand the tree

Other Issues Backtracking Backtracking  chronological backtracking as in Prolog  dependency-directed backtracking Search direction: Search direction:  forward (toward goal)  backward (from goal)  and math proving problems  Bi-directional  and building tunnel story

Search Examples I Game playing Game playing  chess  backgammon Finding path to goal Finding path to goal  Missionaries and cannibals  Towers of Hanoi  Sliding Tile games (15, 8), puzzles

Search Examples II finding a goal finding a goal cryptoarithmetic cryptoarithmetic n-queens n-queens “mutilated checkboard” or “tough nut” of McCarthy problem. “mutilated checkboard” or “tough nut” of McCarthy problem.

Example Applications of search Expert Systems Expert Systems Natural Language Processing Natural Language Processing Vision Vision Robotics: Robotics:  Robot in labyrinth  Robot-human interaction – hands movements  Natural language communication  Imitation of human movement  Robot vision – recognizing boxes in space  Speech processing.

Search: Game Theory 9!+1 = 362,880 Robot interaction with humans, other robots and environment can be described in terms of a game tic-tac-toe,

Application: Game playing programs Programs that: Programs that:  take advantage of the computer's ability to examine a large number of possible moves in a short period of time and  logically assess their probable success or failure have been already developed have been already developed They were used for game playing in: They were used for game playing in:  tic-tac-toe,  checkers,  chess,  and the Oriental game called Go.

Recent Trends in Artificial Intelligence Intelligent agents Intelligent agents  Experimental AI software designed to sift through masses of information made available on the evolving Information Superhighway of cyberspace to suggest topics of interest or importance for an individual. Artificial life Artificial life  A field of AI research that studies the adaptive control systems of insects and other ecological systems and reproduces them in robotic insectoids. More recently researchers have been working on robots with the intelligence of a two-year-old child. More recently researchers have been working on robots with the intelligence of a two-year-old child. Intelligent Agents and Artificial Life are now part of robotics Intelligent Agents and Artificial Life are now part of robotics

Typical tasks in mobile robotics, as related to your projects Search: give an example of search problem that can be solved using Lisp and that has not been presented so far in the class Search: give an example of search problem that can be solved using Lisp and that has not been presented so far in the class  Robot in free space with chairs and people  Robot in maze  Robot’s arm in reaching in space with obstacles Blind and informed search: for your problem, give an example of blind search and informed search. Create a powerful heuristic to solve it. Blind and informed search: for your problem, give an example of blind search and informed search. Create a powerful heuristic to solve it. Games: Games:  examples of games include tic-tac-toe, checkers, chess, go, othello, etc.  What kind of game can be a good choice for our robot-guard in the FAB building environment?  Robot plays scissors-paper-rock game with a human  What other games can be adapted to our mobile robots with hands and heads. These games must be short and visual. Not chess or checkers.

Typical tasks in mobile robotics, as related to your projects Expert system for robot guiding PSU visitors to offices of ECE faculty: Expert system for robot guiding PSU visitors to offices of ECE faculty:  what kind of expert knowledge is needed for our walking guard, in addition to knowledge about the FAB building geometry, distances from room to room?  who sits in which office?  structure of ECE department and its people, etc.?  How is this knowledge stored and accessed?

Possible Programming Problems 1. Mouse in Labyrinth: design an algorithm that will be able to go out of every labyrinth. Discuss the program strategy versus some knowledge of the labyrinth geometry. What kind of concepts introduced so far in the class may be useful? Discuss using depth-first, breadth-first search and other search strategies. 1. Mouse in Labyrinth: design an algorithm that will be able to go out of every labyrinth. Discuss the program strategy versus some knowledge of the labyrinth geometry. What kind of concepts introduced so far in the class may be useful? Discuss using depth-first, breadth-first search and other search strategies. 2. Obstacle Avoiding robot: design a program that will simulate a turtle avoiding obstacles. In contrast to your homework 2, however, the turtle can recognize type of the obstacle and select the best strategy. The best strategy is not necessarily to follow the outline of the obstacle, but to go straight between two obstacles, sometimes closer to one of them. This is a useful sub-problem of avoiding stationary obstacles in the corridor or room for our robot. 2. Obstacle Avoiding robot: design a program that will simulate a turtle avoiding obstacles. In contrast to your homework 2, however, the turtle can recognize type of the obstacle and select the best strategy. The best strategy is not necessarily to follow the outline of the obstacle, but to go straight between two obstacles, sometimes closer to one of them. This is a useful sub-problem of avoiding stationary obstacles in the corridor or room for our robot.  A program like this was written in class. Read about Voronoi diagrams and their uses in robotics.  Think how these diagrams can be improved if you have more knowledge of obstacles.

Download ppt "Robotics Versus Artificial Intelligence. Search. SearchSearch “All AI is search” “All AI is search”  Game theory  Problem spaces Every problem is a."

Similar presentations