"A program that achieves the interpretation of line drawings as polyhedral scenes is described. The method is based on general coherence rules that the surfaces and edges must satisfy, thereby avoiding the use of predetermined interpretations of particular categories of picture junctions and corners." The paper also comments on the relationship of this program to four other scene analysis programs.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California. Revised version in Artificial Intelligence 4:121-137.

Additive AND/OR graphs are defined as AND/ /ORgraphs without circuits, which can be considered as folded AND/OR trees; i.e. the cost of a common subproblem is added to the cost as many times as the subproblem occurs, but it is computed only once. Additive AND/OR graphs are naturally obtained by reinterpreting the dynamic programming method in the light of the problem-reduction approach. An example of this reduction is given. A top-down and a bottom-up method are proposed for searching additive AND/OR graphs. These methods are, respectively, extensions of the "arrow" method proposed by Nilsson for searching AND/OR trees and Dijkstra's algorithm for finding the shortest path. A proof is given that the two methods find an optimal solution whenever a solution exists. 1) introduction In the literature on artificial intelligence, AND/OR trees have proved to be a good formalism for representing the problem-reduction approach to problem solving. Usually, the search is for any solution tree, but in a paper by Nilsson the problem is presented of finding the best solution tree, where arcs have a given cost, and the cost of a tree is simply the sum of the costs of the arcs. Nilsson gives there an algorithm which assumes available, for each node, an estimate of the cost of the "optimal solution tree rooted at that node.

ON THE MECHANIZATION OF ABDUCTIVE LOGIC Harry E. Pople, Jr. Graduate School of Business University of Pittsburgh Pittsburgh, Pennsylvania 15260 Session 6 Logic: II Theorem Proving and Abstract Abduction Is a basic form of logical inference, which is said to engender the use of plans, perceptual models, intuitions, and analogical reasoning - all aspects of Intelligent behavior that have so far failed to find representation in existing formal deductive systems. This paper explores the abductive reasoning process and develops a model for its mechanization, .which An application of the method to the problem of medical diagnosis is discussed. Introduction There has been growing criticism lately concerning the methodology of artificial intelligence. While differing in the specifics of their analyses of the problem, most thoughtful observers seem to feel that the current stock of deductive machinery is simply not up to the task at hand.

In one major section we discuss the imprecision, the incompleteness, the openendedness, and the uncertainty of people's knowledge. In the other major section we discuss strategies people use to make different types of deductive, negative, and functional inferences, and the way uncertainties combine in these inferences. Keywords Semantics, inference, cognitive processes, natural language processing, human memory, question-answering systems, deduction, analogy 1. Introduction In this paper we will discuss how to represent and process information in a computer in ways that are natural to people. This does not mean doing away completely with representations and procedures which computers have traditionally used, but adding new representations and procedures which they have not used. People often store and communicate imprecise, incomplete, and unquantified information; they often assert truth or falsity in relative terms; and they seldom seem to use rigorous logic in their inferential processes. Because of these conditions, people seem to have an almost infinite information processing capacity, with inference making and problem solving abilities more refined and far more flexible than any existing computer program. How can we study these human capabilities in order to make our machines show similar performance? A combination of approaches is perhaps best. Observation of people's behavior, introspection, some experimentation, protocol analysis, and synthesis of computer programs can all be valuable techniques.

The paper deals with the problem of development the multilevel control algorithms fo r six-legged automatic walker, which provide the walker with the possibility to analyse the terrain profile before it while moving over rough terrain , and to synthesize adequate, rather reasonable kinematics of body and legs for walker's locomotion along the route and climbing over obstacles on it s way. DC simulation and analysis of walker's model moving image on DC display screen make it possible to evaluate the algorithms developed and to find ways for their improvement.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

in Roger Schank and Kenneth Colby (eds), Computer Models of Thought and Language, pp. 187-247, San Francisco: W. H. Freeman and Co., 1973

A Universal Modular ACTOR Formalism for Artificial Intelligence Carl Hewitt Peter Bishop Richard Steiger Abstract This paper proposes a modular ACTOR architecture and definitional method for artificial intelligence that is conceptually based on a single kind of object: actors [or, if you will, virtual processors, activation frames, or streams]. The formalism makes no presuppositions about the representation of primitive data structures and control structures. Such structures can be programmed, micro-coded, or hard wired 1n a uniform modular fashion. In fact it is impossible to determine whether a given object is "really" represented as a list, a vector, a hash table, a function, or a process. The architecture will efficiently run the coming generation of PLANNERlike artificial intelligence languages including those requiring a high degree of parallelism. The efficiency is gained without loss of programming generality because it only makes certain actors more efficient; it does not change their behavioral characteristics. The architecture is general with respect to control structure and does not have or need goto, interrupt, or semaphore primitives. The formalism achieves the goals that the disallowed constructs are intended to achieve by other more structured methods. PLANNER Progress "Programs should not only work, but they should appear to work as well." PDP-1X Dogma The PLANNER project is continuing research in natural and effective means for embedding knowledge in procedures. In the course of this work we have succeeded in unifying the formalism around one fundamental concept: the ACTOR. Intuitively, an ACTOR is an active agent which plays a role on cue according to a script" we" use the ACTOR metaphor to emphasize the inseparability of control and data flow in our model. Data structures, functions, semaphores, monitors, ports, descriptions, Quillian nets, logical formulae, numbers, identifiers, demons, processes, contexts, and data bases can all be shown to be special cases of actors. All of the above are objects with certain useful modes of behavior. Our formalism shows how all of the modes of behavior can be defined in terms of one kind of behavior: sending messages to actors. An actor is always invoked uniformly in exactly the same way regardless of whether 1t behaves as a recursive function, data structure, or process.

This paper considers various factors affecting system organization for speech understanding research. The structure of the Hearsay system based on a set of cooperating, independent processes using the hypothesize-and-test paradigm is presented. Design considerations for the effective use of multiprocessor and network architectures in speech understanding systems are presented: control of processes, interprocess communication and data sharing, resource allocation, and debugging are discussed.See also: IEEE Xplore.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

This work deals with computer analysis of textured surfaces. Descriptions of textures are form­alized from natural language descriptions. Local texture descriptions are obtained from the directional and non-directional components of the Fourier transform power spectrum. Analytic expressions are de­rived for orientation, contrast, size, spacing, and in periodic cases, the locations of texture elements. The local descriptions are defined over windows of varying sizes.See also: ACM Digital Library.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, Stanford University Stanford, California, 20-23 August

Eugene, Oregon: Condon Lecture Publications, 1973. Also MIT Al Laboratory, memo 252.