INTRODUCTION Attempts to write'intelligent' computer programs have commonly involved the choice for attack of some particular aspect of intelligent behaviour, together with the choice of some relevant task, or range of tasks, which the program must perform. The emphasis is sometimes on the generality of the program's ability, sometimes on the importance of the particular task which it can perform. Well-known examples of such programs are Newell, Shaw, and Simon's General Problem Solver (1959; see also Ernst and Newell, 1967), which is applicable to a wide range of simple problems, Samuel's checker (draughts) playing program (1959, 1967), and the program written by Evans (1964), which solves geometric analogy problems. However, there is another approach to the goal of machine intelligence which stresses the relationship of an organism to its environment and which sets out from the start to understand what is involved in this relationship. Long ago Grey Walter (1953) experimented with mechanical'tortoises' which could range over the floor in a lifelike manner. Toda (1962), in a whimsical and illuminating paper, has discussed the problems facing an automaton in a simple artificial environment. Friedman (1967), a psychologist, has described a computer simulation of instinctive behaviour involving an automaton equipped with sensory and motor systems. Sandewall (1967) has gone deeply into an automaton/environment relationship with a rather more formal approach. This list is far from complete. In particular, robots of various kinds are under construction at a number of research centres, notably at the Stanford Research Institute (Nilsson and Raphael, 1967). The reader may find it helpful to meditate on the situation of, say, a rat in a cage, as seen by the rat.

The general problem of re-Presentation is concerned with the relationship between different ways of formulating a problem to a problem solving system and the efficiency with which the system can be expected to find a solution to the problem. An understanding of the relationship between problem formulation and problem solving efficiency is a prerequisite for the design of procedures that can automatically choose the most appropriate' representation of a problem (they can find a point of view' of the problem that maximally simplifies the process of finding a solution). Many problems of practical importance are problems of reasoning about actions. In these problems, a course of action has to be found that satisfies a number of specified conditions. A formal definition of this class of problems is given in the next section, in the context of a general conceptual framework for formulating these problems for computers. Everyday examples of reasoning about actions include planning an airplane trip, organizing a dinner party, etc. There are many examples of industrial and military problems in this category, such as scheduling assembly and transportation processes, designing a program for a computer, planning a military operation, etc. The research presented in this paper was sponsored in part by the Air Force Office of Scientific Research, under Contract Number A F49(638)-1184. Part of this work was done while the author was on a visiting appointment at the Computer Science Department of the Carnegie Institute of Technology, Pittsburgh, Pa.

Networks as models of word storage: G. R. Kiss

I shall discuss automatic methods of search for solutions in problems susceptible of a particular formal representation, namely that on which the Graph Traverser program (Doran & Michie 1966, and see Doran p. 105) has been based. In this representation which is essentially that of Newell, Shaw & Simon (1960) a problem consists of a set of states, one or more of which is labelled'goal', together with a rule-book. The rule-book lays down for each state what moves may be made from it to reach other, neighbouring, states. Solution consists in a demonstration that a goal state can be reached from some given initial state via a sequence of intermediate states, sometimes with the additional requirement that an actual sequence, or path, be demonstrated as part of the solution; in this last case, as in the sliding block problem discussed later, the goal (or goals) is typically specified in full as part of the statement of the problem. In other cases, where the goal is only specified in terms of some defining property (such as cost in a transportation problem, or simplicity in a problem of algebraic manipulation) we are interested solely in discovering states possessing this property, and not at all interested in the particular paths leading to them.

A digital computer program, the Graph Traverser (Doran & Michie 1966), can seek a solution to any problem which may be interpreted as that of finding a path from one specified node of a graph to another. Emphasis is placed Upon the evaluation of intermediate states of the problem (nodes of the graph) according to the extent to which they resemble the'goal' state. Sample results from first applications of the program, and possible future developments, are discussed. The program is related to other problemsolving programs. INTRODUCTION: PROBLEMS AND PROBLEM-SOLVING PROGRAMS How to travel from London to Birmingham may, in some circumstances, be a'problem'.

Some of the more popular alternativo used to build knowledge systems are production systems, backward-chained reasoning, logic programming, heuristic search, and the Blackboard framework. Many of the applications implemented in production systems have been written in the OPS language [8]. In this framework, knowledge is represented as a set of homogeneous rules that are scanned for applicability in a data base that contains the current state of solution. Backward chaining also has a homogeneous set of rules, but the search for applicable rules is driven by a hierarchy of goals and sub-goals. The best known system for implementing this type of program is EMYCIN [4].

An increasing number of Artificial Intelligence (Al) programs are implemented on high-performance workstations with a bitmap display, a mouse input device, and a keyboard. The programming environment (usually a dialect of LISP) generally provides support for multiple, overlapping windows, and various kinds of menus including pop up menus. The user can move, reshape, close, and scroll the windows. Additionally, a programmer can designate arbitrary regions of a window to be selectable with the mouse. This means that a user can invoke an action by pressing and releasing a mouse button while the mouse cursor is in the designated region.

In recent years there has been a great deal of interest in the commercial applications of knowledge-based (KB) systems (commonly called expert systems). Interest in KB systems was spurred on by the development of programs that can solve complex tasks at an expert level.

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.

We have de,Jeloped an automated system for the design of laboratory experiments in molecular biology. The system uses a planning method known as skeletal plan refinement that attempts to emulate the human cognitive task of experiment design. This paper describes the theory, history, and implementation of the design system and illustrates its function in the domain of DNA cloning experiments.