We define a semantics for the arithmetic part of PASCAL by giving it

an interpretation in LCF, a language based on the typed λ-calculus.

Programs are represented in terms of their abstract syntax. We show

sample proofs, using LCF, of some general properties of PASCAL and

the correctness of some particular programs. A program implementing

the McCarthy Airline reservation system is proved correct.

In this paper we show that every flowchart program can be written

without 'go-to' statements by using 'while' statements. The main

idea is to introduce new variables to preserve the values of certain

variables at particular points in the program; or alternatively, to

introduce special boolean variables to keep information about the

course of the computation. The new programs preserve the 'topology'

of the original program, and are of the same order of efficiency. We

also show that this cannot be done in general without adding

variables.

A winged edge polyhedron representation is stated and a set of

primitives that preserve Euler's F-E+V=2 equation are explained.

Present use of this representation in Artificial Intelligence for

computer graphics and world modeling is illustrated and its intended

future application to computer vision is described.

This paper describes KRL, a Knowledge Representation Language designed for

use in understander systems. It outlines both the general concepts which

underlie our research and the details of KRL-0, an experimental

implementation of some of these concepts. KRL is an attempt to integrate

procedural knowledge with a broad base of declarative forms. These forms

provide a variety of ways to express the logical structure of the

knowledge, in order to give flexibility in associating procedures (for

memory and reasoning) with specific pieces of knowledge, and to control

the relative accessibility of different facts and descriptions. The

formalism for declarative knowledge is based on %2structured conceptual

objects%* with associated %2descriptions%*. These objects form a network

of %2memory units%* with several different sorts of linkages, each having

well-specified implications for the retrieval process. Procedures can be

associated directly with the internal structure of a conceptual object.

This %2procedural attachment%* allows the steps for a particular operation

to be determined by characteristics of the specific entities involved.

The control structure of KRL is based on the belief that the next

generation of intelligent programs will integrate data-directed and

goal-directed processing by using multi-processing. It provides for a

priority-ordered multi-process agenda with explicit (user-provided)

strategies for scheduling and resource allocation. It provides

%2procedure directories%* which operate along with %2process frameworks%*

to allow procedural parameterization of the fundamental system processes

for building, comparing, and retrieving memory structures. Future

development of KRL will include integrating procedure definition with the

descriptive formalism.

An information processing model of some important aspects of

inductive reasoning is presented within the context of one

scientific discipline. Given a collection of experimental (mass

spectrometry) data from several chemical molecules the computer

program described here separates the molecules into "well-behaved"

subclasses and selects from the space of all explanatory processes

the "characteristic" processes for each subclass. The definitions of

"well-behaved" and "characteristic" embody several heuristics which

are discussed. Some results of the program are discussed which have

been useful to chemists and which lend credibility to this approach.

The program called Heuristic DENDRAL solves scientific induction

problems of the following type: given the mass spectrum of an

organic molecule, what is the most plausible hypothesis of organic

structure that will serve to explain the given empirical data. Its

problem solving power derives in large measure from the vast amount

of chemical knowledge employed in controlling search and making

evaluations.

A brief description of the task environment and the program is given

in Part I. Recent improvements in the design of the program and the

quality of its performance in the chemical task environment are

noted.

The acquisition of task-specific knowledge from chemist-'experts',

the representation of this knowledge in a form best suited to

facilitate the problem solving, and the most effective deployment of

this body of knowledge in restricting search and making selections

have been major foci of our research. Part II discusses the

techniques used and problems encountered in eliciting mass spectral

theory from a cooperative chemist. A sample 'scenario' of a session

with a chemist is exhibited. Part III discusses more general issues

of the representation of the chemical knowledge and the design of

processes that utilize it effectively. The initial, rather straight-

forward, implementations were found to have serious defects. These

are discussed. Part IV is concerned with our presently-conceived

solutions to some of these problems, particularly the rigidity of

processes and knowledge-structures.

An experimental system for automatically generating certain simple

kinds of programs is described. The programs constructed are

expressed in a subset of ALGOL containing assignments, function

calls, conditional statements, while loops, and non-recursive

procedure calls. The input is an environment of primitive programs

and programming methods specified in a language currently used to

define the semantics of the output programming language. The system

has been used to generate programs for symbolic manipulation, robot

control, everyday planning, and computing arithmetical functions.

The objectives of the research reported here are to develop an

automated decision process for real time allocation of defense

missiles to attacking ballistic missiles in general war and to

demonstrate the effectiveness of applying heuristic learning to seek

optimality in the process. The approach is to model and simulate a

missile defense environment and generate a decision procedure

featuring a self-modifying, heuristic decision function which

improves its performance with experience. The goal of the decision

process that chooses between the feasible allocations is to minimize

the total effect of the attack, measured in cumulative loss of

target value. The goal is pursued indirectly by considering the

more general problem of maintaining a strong defense posture, the

ability of the defense system to protect the targets from both

current and future loss.

The objectives of the research reported here are to develop an

automated decision process for real time allocation of defense

missiles to attacking ballistic missiles in general war and to

demonstrate the effectiveness of applying heuristic learning to seek

optimality in the process. The approach is to model and simulate a

missile defense environment and generate a decision procedure

featuring a self-modifying, heuristic decision function which

improves its performance with experience. The goal of the decision

process that chooses between the feasible allocations is to minimize

the total effect of the attack, measured in cumulative loss of

target value. The goal is pursued indirectly by considering the

more general problem of maintaining a strong defense posture, the

ability of the defense system to protect the targets from both

current and future loss.