
State spaces are used for solving problems, such as planning optimal routes for a traveling salesman, determining the shortest distance for a robot arm, or playing a game like chess. A state space can be represented by a graph whose nodes correspond to problem states. Arcs between nodes are representing legal transitions from one problem state to another. Solving the problem means finding a path from a given initial state to a desired solution state, also called goal state, by applying a sequence of transition rules. Each state in the problem is represented by a data structure which may be generated from a preceding data structure by means of a transition rule. This is different from our examples of graphs in the previous chapter where the data structures for nodes were already existing as part of the graph structure. Given a statespace formulation of a problem, there are many approaches to finding a solution path. Two basic search strategies are: depthfirst search and breadthfirst search. A third method, called bestfirst search, uses heuristic information about a problem to select the most promising nodes of a solution path. In this chapter we will discuss the three search strategies.
The depthfirst search strategy has a builtin order for selecting problem states. It selects from the possible states in the problem space always a deepest one. Figure 51 illustrates a number of possible nodes used by a depthfirst search.
The order of the nodes in this state space visited by a depthfirst search is: A, B, E, F, C, D, G, H, I. The framework for solving problems using depthfirst search is very simple (see Figure 52). The specification says that the depthfirst definition has three parameters: an initial or current state, a goal state, and a path representing the current history of states. The depthfirst search may return multiple solutions. If no solution has been found no solution path will be returned. DepthFirst (State, Goal, Path) > multi (Solution); DepthFirst (aGoal, aGoal, aPath) = aGoal & aPath; DepthFirst (aState, aGoal, aPath) = [ NewState = NextMove (aState); DepthFirst (NewState, aGoal, aState & aPath) when not member (NewState, aPath) ];
The meaning of the definition rules of DepthFirst are:
The depthfirst search makes extensive use of backtracking. In particular, the last definition rule may have three reasons to backtrack:
The depthfirst algorithm presented here is independent of the representation of the states in the problem domain; that all is handled by the NextMove instruction. The only other definition which is needed is the member definition, known already from previous chapters on sequences and graphs. To show how the depthfirst strategy works let us consider the problem of Figure 53.            A  B  C  ==>  C  B  A           
There are three tiles on a row, called A, B, and C, representing the initial state of the problem. The transition from one state to another state consists of exchanging two tiles in the row. So, A, B, C may become B, A, C, or A, C, B. The problem is to find all the possible solutions starting from the initial state A, B, C and arriving at the goal state C, B, A. To solve such a statetransition problem the programmer must decide how states are represented. In our example we will use the following term expression to represent a state:
where type letter = term;A, B, C => letter; Now we are able to define the domain specific NextMove operation: NextMove (State) > multi (State); NextMove ( state (L1, L2, L3) ) = ( state (L2, L1, L3), state (L3, L2, L1), state (L1, L3, L2) ); The NextMove definition has three alternatives for the transition of a given state to a new state, as expressed by the serial expression. With this definition we may now complete the program as given in Figure 54. type State = term; type Goal = State; type Path = term; << = sequence >> type Solution = Path;State & Path => Path; << sequence operation >> nil = null(Path);DepthFirst (State, Goal, Path) > multi (Solution); DepthFirst (aGoal, aGoal, aPath) = aGoal & aPath; DepthFirst (aState, aGoal, aPath) = [ NewState = NextMove (aState); DepthFirst (NewState, aGoal, aState & aPath) when not member (NewState, aPath) ];type element = State; type sequence = Path;member (element, sequence) > boolean; member (X, =nil) = false; member (X, X & Tail) = true; member (X, Head & Tail) = member (X, Tail);NextMove (State) > multi (State); NextMove ( state (L1, L2, L3) ) = ( state (L2, L1, L3), state (L3, L2, L1), state (L1, L3, L2) );type letter = term; state (letter, letter, letter) => State; A, B, C => letter;DepthFirst (state (A, B, C), state (C, B, A), nil)? Figure 54: An example of a depthfirst program This program will return the following 9 solutions: state(C, B, A) & (state(B, C, A) & (state(A, C, B) & (state(C, A, B) & (state(B, A, C) & (state(A, B, C) & [ ]))))) state(C, B, A) & (state(C, A, B) & (state(B, A, C) & (state(A, B, C) & [ ]))) state(C, B, A) & (state(B, C, A) & (state(B, A, C) & (state(A, B, C) & [ ]))) state(C, B, A) & (state(C, A, B) & (state(A, C, B) & (state(B, C, A) & (state(B, A, C) & (state(A, B, C) & [ ]))))) state(C, B, A) & (state(A, B, C) & [ ]) state(C, B, A) & (state(B, C, A) & (state(B, A, C) & (state(C, A, B) & (state(A, C, B) & (state(A, B, C) & [ ]))))) state(C, B, A) & (state(C, A, B) & (state(A, C, B) & (state(A, B, C) & [ ]))) state(C, B, A) & (state(B, C, A) & (state(A, C, B) & (state(A, B, C) & [ ]))) state(C, B, A) & (state(C, A, B) & (state(B, A, C) & (state(B, C, A) & (state(A, C, B) & (state(A, B, C) & [ ]))))) There are 4 solutions with 6 states; 4 solutions with 4 states and 1 solution with 2 states. Exercise:
The depthfirst search often works well, as in this example, but there are many ways in which our simple procedure can run into trouble. Whether this will happen or not depends on the state space. Many state spaces are infinite. In such a space, the depthfirst algorithm may miss a goal node, proceeding along an infinite branch of the graph. The program may then indefinitely explore this infinite part of the space never getting closer to a goal. To avoid aimless infinite (noncyclic) branches, we can add a refinement to the basic depthfirst search procedure: limiting the depth of search: DepthFirst (State, Goal, Path, Depth = integer) > multi (Solution); DepthFirst (aGoal, aGoal, aPath, Depth) = aGoal & aPath; DepthFirst (aState, aGoal, aPath, Depth) = [ NewState = NextMove (aState); DepthFirst (NewState, aGoal, aState & aPath, Depth  1) when not member (NewState, aPath) and Depth > 1 ]; The search is not allowed to go in depth beyond the initial Depth limit. This constraint is programmed by decreasing the depth limit at each recursive call, and not allowing this limit to become negative. Another weakness of the depthfirst search is that the path it finds may not be the shortest: since it pursues each branch as far as it can before trying any other, it may find a lengthy solution along one branch when some other branch might have been much shorter, as is illustrated by the results of our example program. Because many interesting problems have too large a search space to be searched exhaustively, a method which delivers shortest paths first is described in the following section.
In contrast to the depthfirst search strategy, the breadthfirst search strategy examines first all nodes at the same level before proceeding to the next level. This results in a search process that tends to develop more into breadth than into depth, as illustrated by Figure 55. A /  \ /  \ B C D / \ /\ / \ /  \ E F G H I The order of the nodes in this state space visited by a breadthfirst search is: A, B, C, D, E, F, G, H, I. The breadthfirst search is not so easy to program as the depthfirst search. The reason for the difference is that the depthfirst search is based on a stack of nodes, which is implicit in the program structure, while the breadthfirst search is keeping all nodes of the same level in a queue, using a firstinfirstout approach which must explicitly be programmed. For example, the nodes on the second level of Figure 55 could be stored in a queue of B, C, D. Additionally, for each element of the queue also a path to its parent node should be maintained in order to use that as a solution path as soon as a goal node has been found. That means that an element of the queue consists of two components: a node and a path to the parent node. As done before, we use the & operation to glue the two components together. That means, for example, that the nodes on the second level of Figure 55 are stored in the queue as: B & path1, C & path1, D & path1 where path1 is represented by A & nil. The nodes on the third level are stored in its queue as:
where path2 is represented by B & A & nil, and path3 is D & A & nil. Each element of the queue represents a path. All the elements of a queue are in fact the nodes of a backward pointing path tree. Based on this approach, the framework for solving problems using breadthfirst search is given in Figure 56. The BreadthFirst definition has two parameters: the queue of state and path information and the goal description. The result may be zero, or more solutions. BreadthFirst (Queue, Goal) > multi (Solution); BreadthFirst (aQueue, aGoal) = [ NewQueue = alist (Path); [ aPath = items (aQueue); aState = State (aPath); if aState <> aGoal then [ NewState = NextMove (aState); add (NewQueue, NewState & aPath) when not member (NewState, aPath) ] else result (aPath) ]; BreadthFirst (NewQueue, aGoal) when not endlist (first (NewQueue)) ];
The procedure starts with the creation of a new empty queue. Then all the path elements of the input queue are read. From each path element the state information is obtained. If the state is not equal to the goal then a new state is determined by calling the domain specific NextMove routine. The path information together with the new state will be added to the new queue if the new state has not been generated before in the current path. If the state is equal to the specified goal then a solution has been found and the associated path will be returned by means of the result statement. After all elements of the input queue have been processed, the BreadthFirst definition will now be called with the new queue unless the new queue is empty. In that case there are no new nodes to be processed and the searching finishes. This procedure works so far, but there are still some improvements to be made. First of all, recursively calling the BreadthFirst routine is not necessary anymore, because the queue has all relevant information for the next cycle of processing. Furthermore, its is even not necessary to create for each level a new queue. One queue for the whole search process is sufficient as long as the reading of the queue elements is progressing from the front onwards and the elements are added at the back end of the queue. Searching stops when the end of the queue has been reached. Our modified framework for breadthfirst search is given in Figure 57. BreadthFirst (State, Goal) > multi (Solution); BreadthFirst (Start, aGoal) = [ Queue = { Start & nil }; [ aPath = items (Queue); aState = State (aPath); if aState <> aGoal then [ NewState = NextMove (aState); add (Queue, NewState & aPath) when not member (NewState, aPath) ] else result (aPath) ]; no (Solution) ];
As an example, we will illustrate the working of this breadthfirst framework on our preceding example of exchanging tiles, with the program of Figure 58. type State = term; type Goal = State; type Path = term; << = sequence >> type Solution = Path;State & Path => Path; << sequence operation >> nil = null(Path);BreadthFirst (State, Goal) > multi (Solution); BreadthFirst (Start, aGoal) = [ Queue = { Start & nil }; [ aPath = items (Queue); aState = State (aPath); if aState <> aGoal then [ NewState = NextMove (aState); add (Queue, NewState & aPath) when not member (NewState, aPath) ] else result (aPath) ]; no (Solution) ];State (Path) > State; State (State & Path) = State;type element = State; type sequence = Path;member (element, sequence) > boolean; member (X, =nil) = false; member (X, X & Tail) = true; member (X, Head & Tail) = member (X, Tail);NextMove (State) > multi (State); NextMove ( state (L1, L2, L3) ) = ( state (L2, L1, L3), state (L3, L2, L1), state (L1, L3, L2) );type letter = term; state (letter, letter, letter) => State; A, B, C => letter;BreadthFirst (state (A, B, C), state (C, B, A) )? Figure 58: An example of a breadthfirst program This program will return the following 9 solutions in increasing order: state(C, B, A) & (state(A, B, C) & [ ]) state(C, B, A) & (state(C, A, B) & (state(B, A, C) & (state(A, B, C) & [ ]))) state(C, B, A) & (state(B, C, A) & (state(B, A, C) & (state(A, B, C) & [ ]))) state(C, B, A) & (state(C, A, B) & (state(A, C, B) & (state(A, B, C) & [ ]))) state(C, B, A) & (state(B, C, A) & (state(A, C, B) & (state(A, B, C) & [ ]))) state(C, B, A) & (state(B, C, A) & (state(A, C, B) & (state(C, A, B) & (state(B, A, C) & (state(A, B, C) & [ ]))))) state(C, B, A) & (state(C, A, B) & (state(A, C, B) & (state(B, C, A) & (state(B, A, C) & (state(A, B, C) & [ ]))))) state(C, B, A) & (state(B, C, A) & (state(B, A, C) & (state(C, A, B) & (state(A, C, B) & (state(A, B, C) & [ ]))))) state(C, B, A) & (state(C, A, B) & (state(B, A, C) & (state(B, C, A) & (state(A, C, B) & (state(A, B, C) & [ ]))))) Exercise:
Our breadthfirst search programs generate solution paths, one after another, ordered according to their lengths. In contrast to the depthfirst strategy the breadthfirst strategy is guaranteed to produce a shortest solution first. However, both strategies suffer from what is called combinatorial explosion. For many problems the number of alternatives to be explored is so high that depthfirst and breadthfirst strategies will not work in a limited amount of time. The reason is that, if each node in the state space has n successors the number of paths at depth d is n^{d} (assuming no cycles). This means that the possible solutions paths grows exponentially with their depth. To limit the number of candidate solution paths problemspecific information is needed to decide what the most promising ways are to proceed at each stage of the search. Search algorithms which are using specific information from the problem domain are called heuristic search strategies; one of them is described in the following section.
The bestfirst search strategy also starts at the start node and maintains a set of candidate paths. It uses a stateevaluation function which computes heuristic estimates for the nodes in the state space. There are several ways to define a stateevaluation function, depending on the application. In the following we will assume that the stateevaluation function is a cost function which is defined for the arcs of the state space and which estimates how far a given node is from another node. Such an estimate indicates how promising a node is with respect to reaching a goal node. The idea is to continue the search always from the most promising node in the candidate set. Bestfirst search is a generalization of breadthfirst search. Both are based on a queue of nodes. The difference is that while the breadthfirst search builds a queue, level for level, in left to right order, the bestfirst search sorts the queue for each new node in increasing cost order and proceeds with the first node of the sorted queue. As a consequence, the queue may contain nodes from different levels and different branches. A framework for bestfirst search is given in Figure 59. It is derived from the breadthfirst search as given in Figure 57. BestFirst (Start = State, Goal = State) > multi (Solution); BestFirst (Start, aGoal) = [ Queue = { cost (Start, 0) & nil }; repeat [ Head = first (Queue); ready when endlist (Head); aPath = remove (Head); aState = State (aPath); Cost = Cost (aPath); if aState <> aGoal then [ NewCost:=0; NewState = NextMove (aState, var (NewCost) ); update (Queue, aPath, NewState, Cost + NewCost) when not member (NewState, aPath) ] else result (aPath) ]; no (Solution) ]; Figure 59: A framework for bestfirst search The main differences with the breadthfirst search are in the area of queue handling and in the processing of cost handling. The procedure starts with the creation of the queue. The first element of the queue consists of the Start node with an initial zero cost value and a nil path. The queue is processed in the following repeat loop. The first element of the queue is removed from the queue and the State and Cost information is obtained from this element. If the state is not the goal then a new state is determined by calling the domain specific NextMove routine. This NextMove definition will also return associated new cost information. If the new state has not been generated before in the current path, the queue will be updated with the new state, path and cost information. The update procedure, as defined in Figure 510, will insert a new element at the right position in an already sorted queue. If the state is equal to the specified goal then a solution has been found and the associated path will be returned by means of the result statement. The processing of the updated queue is repeated until all elements of the queue have been processed. In that case there are no new nodes to be processed and the searching is finished. update (Queue, Path, State, Cost) > nothing; update (aQueue, aPath, aState, Cost) = [ Element:= first (aQueue); repeat [ ready when endlist (Element); anItem = item (Element); Cost1 = Cost (anItem); ready when Cost < Cost1; Element:= next (Element) ]; insert (cost (aState, Cost) & aPath, Element) ]; Figure 510: Updating the queue for bestfirst search Let us investigate how the bestsearch algorithm works for the example of Figure 511. The numbers assigned to the branches of the tree are representing the distances from one node to another. A /  \ 2 / 8 \ 3 /  \ B C D / \ /\ 5 / 2\ 3 /  \ 6 / \ / 2 \ E F G H I Figure 511: Bestfirst search in a state space The bestfirst search starts with the initial node. It expands this node in a breadthfirst manner and sorts the nodes according to the increasing distances. Then the first element of the sorted queue will be removed and its successors will be added to the queue, the queue will be sorted again, and so on. Sorting means in this case the insertion of new elements at the right position in an already sorted queue. The whole process will continue until the queue is empty. For the state space of Figure 511, a queue with the following elements and with the distances between parentheses, are built: A(0) A is removed and expanded: B(2), C(8), D(3) queue is sorted as: B(2), D(3), C(8) B is removed and expanded: E(7), F(4), D(3), C(8) queue is sorted as: D(3), F(4), E(7), C(8) D is removed and expanded: G(6), H(5), I(9), F(4), E(7), C(8) queue is sorted as: F(4), H(5), G(6), E(7), C(8), I(9) The last line represents a queue with only leaf nodes, which successively will be removed from the queue. Leaf nodes will be tested if they are goal nodes. If so, the associated solution path will be returned. As an example, we will illustrate the working of this bestfirst searching framework on one of our preceding examples about determining different routes between Amsterdam and Rome (see Figure 203). We will define a component for the bestfirst searching framework applied to routes between cities (see Figure 512). component SearchBestFirst; type Arc = term; type Node = term; type Cost = integer; type State = term; type Path = term; type Solution = Path; type Graph = list (Arc); arc (Node, Node, Cost) => Arc; SearchBestFirst (Graph, Start= State, Goal= State) > multi (Solution); begin type Queue = list (Path); State & Path => Path; << sequence operation >> cost (State, Cost) => Path; nil = null (Path); graph:= null (Graph);SearchBestFirst (Graph, Start= State, Goal= State) > multi (Solution); SearchBestFirst (Graph, Start, Goal) = [ graph:= Graph; BestFirst (Start, Goal) ];BestFirst (Start = State, Goal = State) > multi (Solution); BestFirst (Start, aGoal) = [ Queue = { cost (Start, 0) & nil }; repeat [ Head = first (Queue); ready when endlist (Head); aPath = remove (Head); aState = State (aPath); Cost = Cost (aPath); if aState <> aGoal then [ NewCost:=0; NewState = NextMove (aState, var (NewCost) ); update (Queue, aPath, NewState, Cost + NewCost) when not member (NewState, aPath) ] else result (aPath) ]; no (Solution) ];update (Queue, Path, State, Cost) > nothing; update (aQueue, aPath, aState, Cost) = [ Element:= first (aQueue); repeat [ ready when endlist (Element); anItem = item (Element); Cost1 = Cost (anItem); ready when Cost < Cost1; Element:= next (Element) ]; insert (cost (aState, Cost) & aPath, Element) ];State (Path) > State; State ( cost(State, Cost) & Path) = State;Cost (Path) > Cost; Cost ( cost(State, integer: Cost) & Path) = Cost;member (State, Path) > boolean; member (State, =nil) = false; member (State, cost (State, Cost) & Path) = true; member (State, cost (State1, Cost) & Path) = member (State, Path);NextMove (State, var (Cost)) > multi (State); NextMove (Node, Cost) = NeighborNode (Node, items(graph), var(Cost));NeighborNode (Node, Arc, var(Cost) ) > optional (Node); NeighborNode (X, arc (X, Y, integer: cost), Cost) = [Cost:= cost; Y]; NeighborNode (X, arc (Y, X, integer: cost), Cost) = [Cost:= cost; Y];end component SearchBestFirst; Figure 512: An example of a bestfirst search component We can now use this component in a dialogue session. We use the information of Figure 203 to fill the map: use SearchBestFirst;Amsterdam, Berlin, Paris, Vienna, Nice, Rome => Node;Map = { arc (Amsterdam, Berlin, 669), arc (Berlin, Vienna, 648), arc (Vienna, Rome, 1150), arc (Amsterdam, Paris, 517), arc (Paris, Vienna, 1271), arc (Paris, Nice, 912), arc (Nice, Vienna, 1130), arc (Nice, Rome, 723)};BestFirst (Amsterdam, Rome)? This program will return the 7 solutions in increasing distance order: cost(Rome, 2152) & (cost(Nice, 1429) & (cost(Paris, 517) & (cost(Amsterdam, 0) & [ ]))) cost(Rome, 2467) & (cost(Vienna, 1317) & (cost(Berlin, 669) & (cost(Amsterdam, 0) & [ ]))) cost(Rome, 2938) & (cost(Vienna, 1788) & (cost(Paris, 517) & (cost(Amsterdam, 0) & [ ]))) cost(Rome, 3170) & (cost(Nice, 2447) & (cost(Vienna, 1317) & (cost(Berlin, 669) & (cost(Amsterdam, 0) & [ ])))) cost(Rome, 3641) & (cost(Nice, 2918) & (cost(Vienna, 1788) & (cost(Paris, 517) & (cost(Amsterdam, 0) & [ ])))) cost(Rome, 3709) & (cost(Vienna, 2559) & (cost(Nice, 1429) & (cost(Paris, 517) & (cost(Amsterdam, 0) & [ ])))) cost(Rome, 4223) & (cost(Nice, 3500) & (cost(Paris, 2588) & (cost(Vienna, 1317) & (cost(Berlin, 669) & (cost(Amsterdam, 0) & [ ]))))) Exercises:
Strategies for searching state spaces are described in any general text on artificial intelligence. Examples are Nilsson(1971, 1980), Winston(1984), Charniak and McDermott(1985), Bratko(1990).

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