SEARCH TREES We will talk about problems with the following characteristics: (i) We could recognise a solution to the problem if given one. Let us call the set of objects which is known to contain the solutions the'candidates'. We include cases where there is more than one solution or where an optimal solution is required. Some examples are: (i) In playing chess there are only a finite number of possible strategies (candidates) but the number is far too large to enumerate. Any assignment is a candidate and there are a finite, but usually large, number of assignments. The candidates are the (n -- I)! permutations of the points omitting the starting point. In this section we will describe in an abstract way two approaches to problems of this type. We will give examples of their use, first in'Some problems about sets' (p. Although both the approaches have often been used before, the discussion may help to clarify those features common to the different applications. Our two approaches are both search techniques (partial enumeration techniques).

MRS stands for Meta-level Representation System. If your response to this is a knowing nod of understanding you can probably skip the first few chapters. In a sense, MRS is a computer language, in that one enters text in a designated syntax and it gets processed and produces answers (or not). But because MRS is also able to reason with the information you give it, the'program' you enter can be seen more as representing facts than specifying a process. The importance and utility of this difference will become clear.

Stanford -- KSL that Think. Consideration of the of the new 16-bit integrated circuits that phenomenal progress of the past 30 years leaves one with a feeling of have allowed computers oi small size and considerable power to be developed. The only certainty in sight is that scientists. BRUCE G. BUCHANAN is Professor of In addition to game playing early Al work focused on techniques for solving Computer Science Research at Stanford small symbolic reasoning problems. Researchers continue to ponder these problems (Overleat) Illustration by f red Nelson as well.

Loosely speaking, recursive inference is when an inference procedure generates an infinite sequence of similar subgoals. In general, the control of recursive inference involves demonstrating that recursive portions of a search space will not contribute any new answers to the problem beyond a certain level. We first review a well known syntactic method for controlling repeating inference (inference where the conjuncts processed are instances of their ruicestors), provide a proof that it is correct, and discuss the con- (Mims under which the strategy is optimal. We also derive more powerful pruning theorems for rases involving transitivity axioms arid cases involving subsumed subgoals. The treatment of repeating inference is followed by consideration of the More difficult prr)liIon of recursive inference Crat does not repeat. Here we show bow knowledge of the properties of the relations involved and knowledge about the contents of the system's database can be used to prove that portions of a search space will not contribute any new .az

A complete program is one that for any environment specifies a unique action for a machine to perform. Programs in most traditional programming languages are complete in this sense. By contrast, a partial program is merely an arbitrary set of constraints on the potential actions of a machine and does not necessarily specify a unique action in every enviranment.

To perform these diagnoses, DART must frequently determine how the hardware's primary inputs can be manipulated to produce desired test conditions at internal nodes. Especially when the system's behavior is time-dependent, this reasoning must be carefully controlled, or a combinatorial explosion may result. This paper contrasts two techniques for representing time-dependent digital system behavior and controlling reasoning to achieve desired hardware states. 2

In this paper I take an empirical look at the question of whether there are rational memckis of discovery and claim that computer programs provida a laboratory for experimentation on this question Recent work in artificial intelligence or Al. has produced programs capaole of serious intellectual work in science Results from Al,viii be used to show that there exist mechanized procedures for discw.ering

No. STAN-CS-82-909 Also numbered: HPP42-7 Department of Computer Science Stanford University Stanford, CA 94305 Abstract This paper describes the student modeler of the GUIDON2 tutor, which understands plan: by a dual search strategy. It first produces multiple predictions of student behavior by a model-driven simulation of the expert. Focused, data-driven searches then explain incongruities. By supplementing each other, these methods lead to an efficient and robust plan understander for a complex domain. Diagnostic problem-solving requires domain knowledge and a plan for applying that knowledge to the problem.

Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques Automatic Programming (AP) Is a new, dynamic, and not precisely defined area of artificial intelligence. This overview discusses the definitions, history, motivating forces and goals of automatic programming and includes a brief description of the basic characteristics and central issues of AP systems. The article begins with a section discussing the various possible definitions of automatic programming, the background in which it has achieved existence, as well as some of its general motivating forces and goals. The next section describes four characteristics of all AP systems: the method by which a user of such a system specifies or describes the desired program, the target language in which the system writes the program, the problem or application area to which the system is addressed, and the approach or operational method employed by the system. Next, a section discusses four basic issues, one or more of which concern all AP systems: the representation and processing of partial or incomplete information; the transformation of structures, and especially the transformation of program descriptions into other descriptions (in this chapter, the term program description includes the user's specification of the desired program, any Internal representations of the progrrm, as well as the target language implementation); the efficiency of the target language Imp,ementation; and the system's capabilities for aiding in the understanding of the program.

Currently Al work is familiar mainly to Its practicing specialists and other interested computer scientists. Yet tho field is of growing interdisciplinary Interest and practical importance. With this book we are trying to build bridges that are easily crossed by engineers, scientists in other fields, ond our own computer science colleagues. In the Handbook we intend to cover the breadth and depth of Al, presenting general overviews of the scientific issues, as well as detailed discussions of particular techniques and important Al systems. Throughout we have tried to keep In mind the reader who is not a specialist In Al.