Multiple-User Quantum Optical Communication

A fundamental understanding of the information carrying capacity of optical channels requires the signal and physical channel to be modeled quantum mechanically. This thesis considers the problems of distributing multi-party quantum entanglement to distant users in a quantum communication system and determining the ability of quantum optical channels to reliably transmit information. A recent proposal for a quantum communication architecture that realizes long-distance, high-fidelity qubit teleportation is reviewed. Previous work on this communication architecture is extended in two primary ways. First, models are developed for assessing the effects of amplitude, phase, and frequency errors in the entanglement source of polarization-entangled photons, as well as fiber loss and imperfect polarization restoration, on the throughput and fidelity of the system. Second, an error model is derived for an extension of this communication architecture that allows for the production and storage of three-party entangled Greenberger-Horne-Zeilinger states. A performance analysis of the quantum communication architecture in qubit teleportation and quantum secret sharing communication protocols is presented. Recent work on determining the channel capacity of optical channels is extended in several ways. Classical capacity is derived for a class of Gaussian Bosonic channels representing the quantum version of classical colored Gaussian-noise channels. The proof is strongly mo- tivated by the standard technique of whitening Gaussian noise used in classical information theory. Minimum output entropy problems related to these channel capacity derivations are also studied. These single-user Bosonic capacity results are extended to a multi-user scenario by deriving capacity regions for single-mode and wideband coherent-state multiple access channels. An even larger capacity region is obtained when the transmitters use non- classical Gaussian states, and an outer bound on the ultimate capacity region is presented

Emacs Lisp in Edwin SScheme

The MIT-Scheme program development environment includes a general-purpose text editor, Edwin, that has an extension language, Edwin Scheme. Edwin is very similar to another general-purpose text editor, GNU Emacs, which also has an extension language, Emacs Lisp. The popularity of GNU Emacs has lead to a large library of tools written in Emacs Lisp. The goal of this thesis is to implement a useful subset of Emacs Lisp in Edwin Scheme. This subset was chosen to be sufficient for simple operation of the GNUS news reading program.

Automated Program Recognition by Graph Parsing

Recognizing standard computational structures (cliches) in a program can help an experienced programmer understand the program. We develop a graph parsing approach to automating program recognition in which programs and cliches are represented in an attributed graph grammar formalism and recognition is achieved by graph parsing. In studying this approach, we evaluate our representation's ability to suppress many common forms of variation which hinder recognition. We investigate the expressiveness of our graph grammar formalism for capturing programming cliches. We empirically and analytically study the computational cost of our recognition approach with respect to two medium-sized, real-world simulator programs.

Program Improvement by Automatic Redistribution of Intermediate Results

Introducing function sharing into designs allows eliminating costly structure by adapting existing structure to perform its function. This can eliminate many inefficiencies of reusing general componentssin specific contexts. "Redistribution of intermediate results'' focuses on instances where adaptation requires only addition/deletion of data flow and unused code removal. I show that this approach unifies and extends several well-known optimization classes. The system performs search and screening by deriving, using a novel explanation-based generalization technique, operational filtering predicates from input teleological information. The key advantage is to focus the system's effort on optimizations that are easier to prove safe.

Automated Program Recognition

The key to understanding a program is recognizing familiar algorithmic fragments and data structures in it. Automating this recognition process will make it easier to perform many tasks which require program understanding, e.g., maintenance, modification, and debugging. This report describes a recognition system, called the Recognizer, which automatically identifies occurrences of stereotyped computational fragments and data structures in programs. The Recognizer is able to identify these familiar fragments and structures, even though they may be expressed in a wide range of syntactic forms. It does so systematically and efficiently by using a parsing technique. Two important advances have made this possible. The first is a language-independent graphical representation for programs and programming structures which canonicalizes many syntactic features of programs. The second is an efficient graph parsing algorithm.

Dependency Directed Reasoning for Complex Program Understanding

Artificial Intelligence research involves the creation of extremely complex programs which must possess the capability to introspect, learn, and improve their expertise. Any truly intelligent program must be able to create procedures and to modify them as it gathers information from its experience. [Sussman, 1975] produced such a system for a 'mini-world'; but truly intelligent programs must be considerably more complex. A crucial stepping stone in AI research is the development of a system which can understand complex programs well enough to modify them. There is also a complexity barrier in the world of commercial software which is making the cost of software production and maintenance prohibitive. Here too a system which is capable of understanding complex programs is a necessary step. The Programmer's Apprentice Project [Rich and Shrobe, 76] is attempting to develop an interactive programming tool which will help expert programmers deal with the complexity involved in engineering a large software system. This report describes REASON, the deductive component of the programmer's apprentice. REASON is intended to help expert programmers in the process of evolutionary program design. REASON utilizes the engineering techniques of modelling, decomposition, and analysis by inspection to determine how modules interact to achieve the desired overall behavior of a program. REASON coordinates its various sources of knowledge by using a dependency-directed structure which records the justification for each deduction it makes. Once a program has been analyzed these justifications can be summarized into a teleological structure called a plan which helps the system understand the impact of a proposed program modification.

CARPS: A Program which Solves Calculus Word Problems

A program was written to solve calculus word problems. The program, CARPS (CALculus Rate Problem Solver), is restricted to rate problems. The overall plan of the program is similar to Bobrow's STUDENT, the primary difference being the introduction of "structures" as the internal model in CARPS. Structures are stored internally as trees. Each structure is designed to hold the information gathered about one object. A description of CARPS is given by working through two problems, one in great detail. Also included is a critical analysis of STUDENT.

SIR: A Computer Program for Semantic Information Retrieval

SIR is a computer system, programmed in the LISP language, which accepts information and answers questions expressed in a restricted form of English. This system demonstrates what can reasonably be called an ability to "understand" semantic information. SIR's semantic and deductive ability is based on the construction of an internal model, which uses word associations and property lists, for the relational information normally conveyed in conversational statements. A format-matching procedure extracts semantic content from English sentences. If an input sentence is declarative, the system adds appropriate information to the model. If an input sentence is a question, the system searches the model until it either finds the answer or determines why it cannot find the answer. In all cases SIR reports its conclusions. The system has some capacity to recognize exceptions to general rules, resolve certain semantic ambiguities, and modify its model structure in order to save computer memory space. Judging from its conversational ability, SIR, is a first step toward intelligent man-machine communication. The author proposes a next step by describing how to construct a more general system which is less complex and yet more powerful than SIR. This proposed system contains a generalized version of the SIR model, a formal logical system called SIR1, and a computer program for testing the truth of SIR1 statements with respect to the generalized model by using partial proof procedures in the predicate calculus. The thesis also describes the formal properties of SIR1 and how they relate to the logical structure of SIR.

ADEPT: A Heuristic Program for Proving Theorems of Group Theory

A computer program, named ADEPT (A Distinctly Empirical Prover of Theorems), has been written which proves theorems taken from the abstract theory of groups. Its operation is basically heuristic, incorporating many of the techniques of the human mathematician in a "natural" way. This program has proved almost 100 theorems, as well as serving as a vehicle for testing and evaluating special-purpose heuristics. A detailed description of the program is supplemented by accounts of its performance on a number of theorems, thus providing many insights into the particular problems inherent in the design of a procedure capable of proving a variety of theorems from this domain. Suggestions have been formulated for further efforts along these lines, and comparisons with related work previously reported in the literature have been made.

A Planning System for Robot Construction Tasks

This paper describes BUILD, a computer program which generates plans for building specified structures out of simple objects such as toy blocks. A powerful heuristic control structure enables BUILD to use a number of sophisticated construction techniques in its plans. Among these are the incorporation of pre-existing structure into the final design, pre-assembly of movable sub-structures on the table, and use of the extra blocks as temporary supports and counterweights in the course of construction. BUILD does its planning in a modeled 3-space in which blocks of various shapes and sizes can be represented in any orientation and location. The modeling system can maintain several world models at once, and contains modules for displaying states, testing them for inter-object contact and collision, and for checking the stability of complex structures involving frictional forces. Various alternative approaches are discussed, and suggestions are included for the extension of BUILD-like systems to other domains. Also discussed are the merits of BUILD's implementation language, CONNIVER, for this type of problem solving.

Flexibility and Efficiency in a Computer Program for Designing Circuits

This report is concerned with the problem of achieving flexibility (additivity, modularity) and efficiency (performance, expertise) simultaneously in one AI program. It deals with the domain of elementary electronic circuit design. The proposed solution is to provide a deduction-driven problem solver with built-in-control-structure concepts. This problem solver and its knowledge base in the applicaitn areas of design and electronics are descrbed. The prgram embodying it is being used to explore the solutionof some modest problems in circuit design. It is concluded that shallow reasoning about problem-solver plans is necessary for flexibility, and can be implemented with reasonable efficiency.