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If one chooses to say that a current state is the state of having a particular goal, the distinction naturally vanishes. Much human problem- solving behavior is observed to involve reasoning backward, and many artificial intelligence programs are based on this general strategy. In addition, combinations of forward and backward reasoning are possible. One important AI technique involving forward and backward reasoning is called means- ends analysis; it in- volves comparing the current goal with a current task- domain situation to extract a difference between them.

The Soul and the Harpy. Reflections on the Aims and Methods of Literary Historiography

This difference is then used to index the forward operator most relevant to reducing the difference. If this especially relevant operator cannot be immediately applied to the present problem state, subgoals are set up to change the problem state so that the relevant operator can be applied. After these subgoals are solved, the relevant operator is applied and the resulting, modified situation becomes a new starting point from which to solve for the original goal.

See Articles n. D2 and n. State Spaces and Problem Reduction A problem- solving system that uses forward reasoning and whose operators each work by producing a single new object — a new state — in the database is said to represent problems in a state- space repre- sentation see Article n. For backward reasoning, a distinction may be drawn between two cases.

In one, each application of an operator to a problem yields exactly one new problem, whose size or difficulty is typically slightly less than that of the previous problem. Systems of this kind will also be referred to, in this chapter, as employing state-space representations. Two instances of such representations are presented later in the chapter.

One example is the Logic Theorist program Article n. Di ; the other is the backward-reasoning part of Pohl's bidirectional search Articles n.

Harpy, production systems and human cognition

Cl and n. A more complex kind of backward reasoning occurs if applying an operator may divide the problem into a set of subproblems, perhaps each significantly smaller than the original. A system using this kind of backward reasoning, distinguished by the fact that its operators can change a single object into a conjunction of objects, will be said to employ a problem-reduction representation. The relation between problem-reduction and state-space representations is examined further at the end of Article n.

There may or may not be constraints on the order in which the subproblems generated by a problem-reduction system can be solved. Applying the obvious operator changes it to the new problem consisting of two integrations, f x dx and g x dx.

Depending on the representation, the new problem can be viewed as made up of either a two integration subproblems that can be solved in any order or b two integration subproblems plus the third subproblem of summing the integrals. In the latter case, the third task cannot be done until the first two have been completed. Besides the state- space and problem-reduction approaches, other variations on problem representation are possible. One is used in game- playing problems, which differ from most other problems by virtue of the presence of adversary moves.

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A game-playing problem must be represented in a way that takes into account the opponent's possible moves as well as the player's own. The usual representation is a game tree see Article n. B3 , which shares many features of a problem- reduction representation. Another variation is relevant to theorem- proving systems, many of which use forward reasoning and operators rules of inference that act on conjunctions of objects in the database.

Although the representations discussed here assume that each operator takes only a single object as input, it is possible to define a theorem- proving representation that provides for multiple-input, single-output operators Kowalski, ; see also Chap, xn, in Vol. Graph Representation In either a state-space or a problem-reduction representation, achieving the desired goal can be equated with finding an appropriate finite sequence of applications of available operators.

While what one is primarily interested in — the goal situation or the sequence that leads to it— may depend on the problem, the term search can always be under- stood, without misleading consequences, as referring to the search for an appropriate operator sequence.

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In a state- space representation, a tree may be used to represent the set of problem states produced by operator applications. In such a representation, the root node of the tree represents the initial problem situation or state.

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Each of the new states that can be produced from this initial state by the application of just one operator is represented by a successor node of the root node. Subsequent operator applications produce successors of these nodes, and so on. Each operator application is represented by a directed arc of the tree. In general, the states are represented by a graph rather than by a tree, since there may be different paths from the root to any given node.

Trees are an important special case, however, and it is usually easier to explain their use than that of graphs. See Article n.

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The Search Space The problem of producing a state that satisfies a goal condition can now be formulated as the problem of searching a graph to find a node whose associated state description satisfies the goal. It should be noted that there is a distinction between the graph to be searched and the tree or graph that is constructed as the search proceeds.

In the latter, nodes and arcs can be represented by explicit data structures; the only nodes included are those for which paths from the initial state have actually been discovered. This explicit graph, which grows as the search proceeds, will be referred to as a search graph or search tree. In contrast, the graph to be searched is ordinarily not explicit.


It may be thought of as having one node for every state to which there is a path from the root. It may even be thought of, less commonly, as having one node for every state that can be described, whether or not a path to it exists. Clearly, many problem domains A Overview 27 such as theorem proving have an infinite search space, and the search space in others, though finite, is unimaginably large.

Estimates of search- space size may be based on the total number of nodes however defined or on other measures.


In chess, for example, the number of different complete plays of the average-length game has been estimated at 10 Shannon, , , although the number of "good" games is much smaller see Good, Even for checkers, the size of the search space has been estimated at 10 40 Samuel, Searching now becomes a problem of making just enough of the search space explicit in a search graph to contain a solution of the original goal. If the search space is a general graph, the search graph may be a subgraph, a subgraph that is also a tree, or a tree obtained by representing distinct paths to one search space node with duplicate search graph nodes.

Limiting Search The critical problem of search is the amount of time and space necessary to find a solution. As the chess and checkers estimates suggest, exhaustive search is rarely feasible for nontrivial problems. Examining all sequences of n moves, for example, would require operating in a search space in which the number of nodes grows exponentially with n. Such a phenomenon is called a combinatorial explosion. There are several complementary approaches to reducing the number of nodes that a search must examine.

One important way is to recast the problem so that the size of the search space is reduced. A dra- matic, if well-known, example is the mutilated chessboard problem: Suppose two diagonally opposite corner squares are removed from a standard 8 by 8 square chessboard. Can 31 rectangular dominoes, each the size of exactly two squares, be so placed as to cover precisely the remaining board? Raphael, , p. If, however, one observes that every domino placed must cover both a red square and a black one and that the squares removed are both of one color, the answer is immediate.

Unfortunately, little theory exists about how to find good problem representations. Some of the sorts of things such a theory would need to take into account are explored by Amarel , who gives a sequence of six representations for a single problem, each re- ducing the search-space size by redefining the states and operators.

Several graph- and tree- searching methods have been developed, and these play an important role in the control of problem- solving processes. Of special interest are those graph- searching methods that use heuristic knowledge from the problem domain to help focus the search. In some types of problems, these heuristic search techniques can prevent a combinatorial explosion of possible solutions. Heuristic search is one of the key contributions of AI to efficient problem solving.

Various theorems have been proved about the properties of search techniques, both those that do and those that do not use heuristic information. Briefly, it has been shown that certain types of search methods are guaranteed to find optimal solutions when such exist. Some of these methods, under certain comparisons, have also been shown to find solutions with minimal search effort.

Graph- and tree- searching algorithms, with and without the use of heuristic information, are discussed at length in Section n. A third approach addresses the question: Given one representation of a search problem, can a problem- solving system be programmed to find a better representation automatically?

The question differs from that of the first approach to limiting search in that here it is the program, not the program designer, that is asked to find the improved representation. STRIPS augments its initial set of operators by discovering, generalizing, and remembering macro- operators, composed of sequences of primitive operators, as it gains problem- solving experience.

D6 , which implements the idea of planning, in the sense of defining and solving problems in a search space from which unimportant details have been omitted. The details of the solution are filled in by smaller searches within the more detailed space only after a satisfactory outline of a solution, or plan, has been found. The Meaning of Heuristic and Heuristic Search Although the term heuristic has long been a key word in AI, its meaning has varied both among authors and over time.

In general, its usage is illustrated by example better than by definition, and several of the prime examples are included in the programs of Section n. However, a brief review of the ways heuristic and heuristic search have been used may provide a useful warning against taking any single definition too seriously.

A Overview 29 As an adjective, the most frequently quoted dictionary definition for heuristic is "serving to discover. When the term came into use to describe AI techniques, some writers made a distinction between methods for discovering solutions and methods for producing them algorithmically. Thus, Newell, Shaw, and Simon stated in "A process that may solve a given problem, but offers no guarantees of doing so, is called a heuristic for that problem" Newell, Shaw, and Simon, b, p.

But this meaning was not universally accepted. Minsky, for example, said in a paper: The adjective "heuristic," as used here and widely in the literature, means related to improving problem-solving performance; as a noun it is also used in regard to any method or trick used to improve the efficiency of a problem-solving program.