For the past several years research on robot problem-solving methods has centered on what may one day be called'simple' plans: linear sequences of actions to be performed by single robots to achieve single goals in static environments. This process of forming new subgoals and new states continues until a state is produced in which the original goal is provable; the sequence of operators producing that state is the desired solution. In the case of a single goal wff, the objective is quite simple: achieve the goal (possibly while minimizing some combination of planning and execution cost). The objective of the system is to achieve the single positive goal (perhaps while minimizing search and execution costs) while avoiding absolutely any state satisfying the negative goal.

In forming such a view the Council has available to it a great deal of specialist information through its structure of Boards and Committees-- particularly from the Engineering Board and its Computing Science Committee and from the Science Board and its Biological Sciences Committee. To supplement the important mass of specialist and detailed information available to the Science Research Council, its Chairman decided to commission an independent report by someone outside the Al eld but with substantial general experience of research work in multidisciplinary elds including elds with mathematical, engineering and biological aspects. Such a personal view of the subject might be helpful to other lay persons such as Council members in the process of preparing to study specialist reports and recommendations and working towards detailed policy formation and decision taking. In scientic applications, there is a similar look beyond conventional data processing to the problems involved in large-scale data banking and retrieval, The vast eld of chemical compounds is one which has lent itself to ingenious and eective programs for data storage and retrieval and for the inference of chemical structure from mass-spec- trometry and other data.

It seems most unlikely that one could in general write purely applicative Schonfmkel descriptions', like (5), of functions already known to one in some other form. One makes assertions in the system by writing clauses, i.e., finite collections of literals considered as disjunctions of their members, universally quantified with respect to all variables. In other words, this is a first-order language in which there is only one relation symbol, namely equality; only one function symbol, namely application; and a collection of individual constants. In particular the resolution principle may be used as sole principle; or the resolution principle together with paramodulation (Robinson and Wos 1969); or Sibert's system (Sibert 1969); or the E-resolution system of Morris (1969).

Before discussing in detail an example of a simple heuristic problemsolving program, the Graph Traverser, I shall give an indication of the most important work carried out in this field, stressing the more fundamental ideas. Bobrow (1964) describes a question-answering system for high school algebra word problems'. I shall distinguish four: (1) the external application problem to which the program has been applied; (2) the internal problem which the program generates from the application problem and which it must solve in order to produce a solution to the application problem (this internal problem will be an'idealised' version of the application problem, and will typically vary little from one application to another. It is important to realise that more than one internal problem may be capable of derivation from a given application problem); (3) the strategy which the program uses to solve the internal problem; (4) the translation process from the application problem to the internal problem.

Much of classical and contemporary analysis stems from this source: iteration, ergodic theory, the theory of semigroups [1], the theory of branching processes [2], random transformations at fixed times and deterministic transformations at stochastic times [3, 4]. Let us now describe a dynamic programming process of discrete, deterministic type. This is an extremely important observation since it enables us to employ a type of approximation not available in classical analysis, approximation in policy space. A particular class of problems of this type involves ordinary and partial differential operators and is related both to the theory of differential inequalities inaugurated by Caplygin and Lyapunoy [15, 16], and to the modern maximum principles of partial differential equations.

This article is concerned with the psychology of human thinking. It setsforth a theory to explain how some humans try to solve some simpleformal problems. The research from which the theory emerged is intimatelyrelated to the field of information processing and the construction of intelligentautomata, and the theory is expressed in the form of a computerprogram. The rapid technical advances in the art of programming digitalcomputers to do sophisticated tasks have made such a theory feasible.It is often argued that a careful line must be drawn between the attemptto accomplish with machines the same tasks that humans perform, andthe attempt to simulate the processes humans actually use to accomplishthese tasks. The program discussed in the report, GPS (General ProblemSolver), maximally confuses the two approachesâwith-mutual"!benefit. Lerende Automaten, Munich: Oldenberg KG

These are (1) based on the use of conditional probabilities, (2) suggested by the idea that biological learning is due to facilitation of synapses and (3) based on existing statistical theory dealing with the optimisation of operating conditions. Although the application of logical-type machines to process control involves formidable complexity, design principles are evolved here for a learning machine which deals with quantitative signal and depends for its operation on the computation of correlation coefficients. By such trial-and-error procedures the control functions can be made to approach their optimum forms. THE CONDITIONAL PROBABILITY COMPUTER The ideas of probability theory must obviously be involved in any empirical approach to process control, since the aim is to maximise the probability of the desired goal in the future.

This ancient branch of knowledge is represented by the medical practitioner; we shall therefore establish the logical structure of Medicine by studying his activities. If we confine ourselves to the traditional system, the consultation consists of various parts, as follows: the questioning, the general examination, palpation, inspection, examination with instruments. Much emphasis is laid upon the value of a proper examination, a complete record of symptoms, palpation carried out gently and correctly, but no indication is given of the way in which all this material is put together. We shall call all the actions by which the doctor obtains information about his patient the "acquisition of information", which thus comprises the general examination, palpation, questioning the patient, special examinations: in brief, all the serdological and laboratory techniques.

The Dawn of Dynamic Programming Richard E. Bellman (1920–1984) is best known for the invention of dynamic programming in the 1950s. During his amazingly prolific career, based primarily at The University of Southern California, he published 39 books (several of which were reprinted by Dover, including Dynamic Programming, 42809-5, 2003) and 619 papers. Professor Bellman was awarded the IEEE Medal of Honor in 1979 "for contributions to decision processes and control system theory, particularly the creation and application of dynamic programming." The IEEE citation continued: "Richard Bellman is a towering figure among the contributors to modern control theory and systems analysis.