The Platonic assumption that the world is made up entirely of objects with flat surfaces obviously does not hold; and yet, as with so many other simplifications of reality for the sake of tractability, it has been immensely productive in establishing a paradigm for scene analysis. There is a coherent evolving body of research based on the notion that a polyhedral world is the simplest we can consider without eliminating any of the essential aspects of scene analysis, namely, the picture-taking process, models, lighting, support, occlusion, and so on. The thesis is that once we achieve ways of dealing intelligently with those aspects for a simple, but nonetheless real, world we could then consider the fuzzy world of teddy bears (Michie, 1974) and the like. This should not be taken as suggesting that each of those aspects presents simply a separate, independent subproblem to be solved. The most important question to be faced was how to write programs that coordinate the use of these separate, but interrelated, knowledge systems to achieve sensible picture interpretations. Roberts (Roberts, 1965) was the first to give an answer to this question. We shall examine his answer in some detail, because he exposed in it the issues that became themes of the first decade of scene analysis.

The Open Mind Common Sense project is an attempt to construct a database of commonsense knowledge through the collaboration of a distributed community of thousands of non-expert netizens. We give an overview of the project, describe our knowledge acquisition and representation strategy of using natural language rather than formal logic, and demonstrate this strategy with a search engine application that employs simple commonsense reasoning to reformulate problem queries into more effective solution queries.

Earlier work by Newell, Simon, Shaw, and Gelernter in the middle and late 1950s emphasized the heuristic approach, but the weight soon shifted to various syntactic methods culminating in a large effort on resolution type systems in the last half of the 1960s. It was about 1970 when considerable interest was revived in heuristic methods and the use of human supplied, domain dependent, knowledge. It is not my intention here to slight the great names in automatic theorem proving, and their contributions to all we do, but rather to show another side of it. For recent books on automatic theorem proving see Chang and Lee [19], Loveland [44], and Hayes [31]. Also see Nilsson's recent review article [61]. The word "resolution" has come to be associated with general purpose types of theorem provers which use very little domain dependent information and few if any special heuristics besides those of a syntactic nature. It has also connoted the use of clauses and refutation proofs. There was much hope in the late 60's that such systems, especially with various exciting improvements, such as set of support, model elimination, etc., would be powerful provers. But by the early 70's there was emerging a belief that resolution type systems could never really "hack" it, could not prove really hard mathematical theorems, without some extensive changes in philosophy.

Many of the recent expert rule-based systems [Dendral, Mycin, AM, Pecos] have architectures that differ significantly from the simple domainindependent architectures of "pure" production systems. The purpose of this paper is to explore, somewhat more systematically than has been done before, the various ways in which the simplicity constraints can be relaxed, and the benefits of doing so. The most significant benefits arise from three sources: (i) the grain size of a typical rule can be increased until it captures a unit of advice which is meaningful in that system's task domain, (ii) the interpreter can become accessible to the rules and thus become dynamically modifiable, and (iii) meaningful permanent Knowledge can be stored in data memories, not just within productions. Although there are costs associated with relaxing the simplicity constraints, for many task domains the benefits outweigh the costs.

Philosophers and - "pseudognosticians" (the artificial intelligentsial) are coming more and more to recognize that they share common ground and that each can learn from the other. This has been generally recognized for many years as far as symbolic logic is concerned, but less so in relation to the foundations of probability. In this essay I hope to convince the pseudognostician that the philosophy of probability is relevant to his work. Formal systems, such as those used in mathematics, logic, and computer programming, can lead to deductions outside the system only when there is an input of assumptions. For example, no probability can be numerically inferred from the axioms of probability unless some probabilities are assumed without using the axioms: ex nihilo nihil fit.2

"Artificial intelligence tasks which can be formulated as constraint satisfaction problems, with which this paper is for the most part concerned, are usually solved by backtracking. By examining the thrashing behavior that nearly always accompanies backtracking, identifying three of its causes and proposing remedies for them we are led to a class of algorithms which can profitably be used to eliminate local (node, arc and path) inconsistencies before any attempt is made to construct a complete solution. A more general paradigm for attacking these tasks is the alternation of constraint manipulation and case analysis producing an OR problem graph which may be searched in any of the usual ways.Many authors, particularly Montanan i and Waltz, have contributed to the development of these ideas; a secondary aim of this paper is to trace that history. The primary aim is to provide an accessible, unified framework, within which to present the algorithms including a new path consistency algorithm, to discuss their relationships and the many applications, both realized and potential, of network consistency algorithms."See also: sciencedirect.comArtificial Intelligence 8:99-118

Further progress in the application of computers to many practical fields seems to depend heavily on the success in implementing learning and inductive processes within machines. For example, to develop a consultation system for medical or plant disease diagnosis, prognosis and decision making in general, it is very desirable, perhaps even necessary, to be able to'teach' the system through examples of correct and/or incorrect decisions, rather than by precisely describing the decision process in its full generality and then transforming this description into a computer program. A similar situation exists in computer chess. The development of computer programs playing at the master level (especially the end games) seems to be a formidable task if the programs are not eventually able to learn and improve on their decision making rules through the specific examples of games, rather than by being explicitly told all the rules. Due to easy access to human knowledge about chess and the relative simplicity of testing the results, chess is one of the most attractive testing domains for inductive inference programs.

EPISTEMOLOGICAL PROBLEMS OF ARTIFICIAL INTELLIGENCE John McCarthy Computer Science Department Stanford University Stanford, California 94305 Introduction In (McCarthy and Hayes 1969), we proposed dividing the artificial intelligence problem into two parts - an epistemological part and a heuristic part. This lecture further explains this division, explains some of the epistemological problems, and presents some new results and approaches. The epistemological part of Al studies what kinds of facts about the world are available to an observer with given Opportunities to observe, how these facts can be represented in the memory of a computer, and what rules permit legitimate conclusions to be drawn from these facts. It leaves aside the heuristic problems of how to search spaces of possibilities and how to match patterns. Considering epistemological problems separately has the following advantages: I. The same problems of what information is available to an observer and what conclusions can be drawn from information arise in connection with a variety of problem solving tasks. Recently we have found that introducing concepts as individuals makes possible a first order logic expression of facts usually expressed In modal logic but With important advantages over modal logic - and so far no disadvantages.

In an idealized picture of a scene that contains only polyhedra each line segment that is recorded can have only one of four possible "meanings". In order to understand the picture it is necessary that we be able to label each line with one of the four corresponding labels:, or A " " or "." label is associated, respectively, with a convex or concave edge that has both of its two associated planes visible. A line labelled with an arrow refers to a convex edge oriented so that only one of these two planes is visible from the camera and the other is hidden behind it. The orientation of the arrow along the line is such that the planes are to the right of the arrow. If no consistent set of line labels is possible the picture is of an "impossible"--object.

To use language one must be able to make inferences about the information which language conveys. This is apparent in many ways. For one thing, many of the processes which we typically consider "linguistic" require inference making. For example, structural disambiguation: (1) Waiter, I would like spaghetti with meat sauce and wine. You would not expect to be served a bowl of spaghetti floating in meat sauce and wine. That is, you would expect the meal represented by structure (2) rather than that represented by (3).