Artificial Intelligence Lecture Notes PDF

Instructor Prof. Shereen Aly Taie 1  states? operators? goal test? path cost?  states? locations of tiles operators? move blank left, right, up, down goal test? = goal state (given) path cost? 1 per move  states? operators? goal test? path cost? ...

Instructor Prof. Shereen Aly Taie 1  states? operators? goal test? path cost?  states? locations of tiles operators? move blank left, right, up, down goal test? = goal state (given) path cost? 1 per move  states? operators? goal test? path cost?  states? 2 integers to represent amount of dirt in each room and one value to represent robot location operators? Left, Right, Suck goal test? no dirt at all locations path cost? 1 per action  Given a problem representation, search is the process that builds some or all of the search tree for the representation in order to do one of the following: – If the search tree has one or more goals, identify one (or all of them) and the sequence of operators that produce each. – If the search tree has one or more goals, identify a least costly one and the sequence of operators that produces it. – If the search tree is finite and does not contain a goal, recognise this. 7  Breadth-first search  Depth-first search  Iterative deepening search  Bidirectional search  Uniform-cost search  Also known as blind search  Greedy search  A* search  IDA* search  Hill climbing  Simulated annealing  Also known as heuristic search ◦ require heuristic function Blind Searches  Simply searches the State Space  Can only distinguish between a goal state and a non-goal state  Sometimes called an uninformed search as it has no knowledge about its domain  Blind Searches have no preference as to which state (node) that is expanded next  The different types of blind searches are characterised by the order in which they expand the nodes.  Expand Root Node First  Expand all nodes at level 1 before expanding level 2  OR  Expand all nodes at level d before expanding nodes at level d+1 14 15 Expand Node list node {A} A BCD B EF D GHI I JK K LM 16 17 Search Tiers S d e p b c e h r q a a h r p q f p q f q c G q c G a [demo: bfs] a  Branching rate ◦ Average number of edges coming from a node (3 above)  Uniform Search ◦ Every node has same number of branches (as above)  a G b c Strategy: expand e a shallowest d f S h node first p q r Implementation: Fringe is a FIFO queue S d e p Search b c e h r q Tiers a a h r p q f p q f q c G q c G a a Given the following state space (tree search), give the sequence of visited nodes when using BFS (assume that the node O is the goal state): A B C D E F G H I J K L M N O 23  A, A B C D E 24  A, B, A B C D E F G 25  A, B,C A B C D E F G H 26  A, B,C,D A B C D E F G H I J 27  A, B,C,D,E A B C D E F G H I J 28  A, B,C,D,E, F, A B C D E F G H I J 29  A, B,C,D,E, F,G A B C D E F G H I J K L 30  A, B,C,D,E, F,G,H A B C D E F G H I J K L 31  A, B,C,D,E, F,G,H,I A B C D E F G H I J K L M 32  A, B,C,D,E, F,G,H,I,J, A B C D E F G H I J K L M N 33  A, B,C,D,E, F,G,H,I,J, K, A B C D E F G H I J K L M N 34  A, B,C,D,E, F,G,H,I,J, K,L A B C D E F G H I J K L M N O 35  A, B,C,D,E, F,G,H,I,J, K,L, M, A B C D E F G H I J K L M N O 36  A, B,C,D,E, F,G,H,I,J, K,L, M,N, A B C D E F G H I J K L M N O 37  A, B,C,D,E, F,G,H,I,J, K,L, M,N, A Goal state: O B C D E F G H I J K L M N O 38 The returned solution is the sequence of operators in the path: A, B, G, L, O A B C D E F G H I J K L M N O 39  The example node set Initial state A B C D E F Goal state G H I J K L M N O P Q R S T U V W X Y Z Press space to see a BFS of the example node set  A B C D E F G H I J K L M N O P Q R S T U BREADTH-FIRST SEARCH PATTERN  Observations ◦ Very systematic ◦ If there is a solution breadth first search is guaranteed to find it ◦ If there are several solutions then breadth first search will always find the shallowest goal state first and if the cost of a solution is a non- decreasing function of the depth then it will always find the cheapest solution 43  a G b c Strategy: e expand a d f deepest node S h first p r q Implementation : Fringe is a LIFO S stack d e p b c e h r q a a h r p q f p q f q c G q c G a a  If DFS goes down a infinite branch it will not terminate if it does not find a goal state.  If it does find a solution there may be a better solution at a lower level in the tree. Therefore, depth first search is neither complete nor optimal.  DFS may never terminate as it could follow a path that has no solution on it  DLS solves this by imposing a depth limit, at which point the search terminates that particular branch  Can be implemented by the general search algorithm using operators which keep track of the depth  Choice of depth parameter is important ◦ Too deep is wasteful of time and space ◦ Too shallow and we may never reach a goal state  Suppose branching rate b  Breadth-first ◦ Complete (guaranteed to find solution) ◦ Requires a lot of memory  At depth d needs to remember up to bd-1 states  Depth-first ◦ Not complete because of indefinite paths or depth limit ◦ But is memory efficient  Only needs to remember up to b*d states