We present a prototype natural-language problem-solving application for a financial services call center, developed as part of the Amitiés multilingual human-computer dialogue project. Our automated dialogue system, based on empirical evidence from real call-center conversations, features a data-driven approach that allows for mixed system/customer initiative and spontaneous conversation. Preliminary evaluation results indicate efficient dialogues and high user satisfaction, with performance comparable to or better than that of current conversational travel information systems.

Article Free Access Share on Home-study software: flexible, interactive, and distributed software for independent study Authors: Christopher Connelly Electrotechnical Laboratory, Umezono, 1-1-4,Tsukuba City, Ibaraki, Japan and Department of Computer Science, Duke University, Durham, NC Electrotechnical Laboratory, Umezono, 1-1-4,Tsukuba City, Ibaraki, Japan and Department of Computer Science, Duke University, Durham, NCView Profile , Alan W. Biermann Department of Computer Science, Duke University, Durham, NC Department of Computer Science, Duke University, Durham, NCView Profile , David Pennock Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor and Department of Computer Science, Duke University, Durham, NC Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor and Department of Computer Science, Duke University, Durham, NCView Profile , Peter Wu Microsoft Corporation, Menlo Park, CA and Department of Computer Science, Duke University, Durham, NC Microsoft Corporation, Menlo Park, CA and Department of Computer Science, Duke University, Durham, NCView Profile Authors Info & Claims SIGCSE '96: Proceedings of the twenty-seventh SIGCSE technical symposium on Computer science educationMarch 1996Pages 63–67https://doi.org/10.1145/236452.236509Published:01 March 1996Publication History 10citation218DownloadsMetricsTotal Citations10Total Downloads218Last 12 Months30Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

In a world saturated with computers, it is important that the popnlace have some understanding of what thesedevices am, how they work, what they can do, and what they cannot do.People will not intelligently

Associated with each learning system there is a class of learnable behaviors. If the target behavior to be acquired is in the learnable class, it will be learned perfectly. If it is outside that class, the machine will only be able to acquire a behavior that approximates the target and it will always make errors. It is desirable for a learning machine to have a large learnable class to maximize the chances of acquiring the unknown behavior and to minimize the expected error when only an approximation is possible. However, it is also desirable to have a small learnable class so that learning can be achieved rapidly. Thus the design of learning machines involves selecting a position on the spectrum: minimum error and slow learning time versus larger error and faster learning time. A computational method is given for finding where a given learning machine is on this spectrum. Machines that have fast learning times, relatively small learnable classes, and thus relatively large expected errors are called realization sparse in this article. These machines do little better than a random coin flipping algorithm in many situations. It is shown that many common learning systems are of this type including signature tables, linear system models, and conjunctive normal form expression based systems. These studies lead to the concept of an “optimum” machine which spreads its learnable behaviors across the behavior space in a manner to minimize the expected error. an approximation to such optimum machines is presented and its behavior is compared to the more traditional learning machines. © 1994 John Wiley & Sons, Inc.

article Free Access Share on An overview course in academic computer science: a new approach for teaching nonmajors Author: Alan W. Biermann Department of Computer Science, Duke University, Durham, North Carolina Department of Computer Science, Duke University, Durham, North CarolinaView Profile Authors Info & Claims ACM SIGCSE BulletinVolume 22Issue 1Feb. 1990 pp 236–239https://doi.org/10.1145/319059.323462Published:01 February 1990Publication History 10citation186DownloadsMetricsTotal Citations10Total Downloads186Last 12 Months10Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

An imperative sentence processor that enables a user to manipulate text with connected speech and touch-graphics input is described. The processor includes capabilities to follow dialogue focus, execute a variety of imperative commands, and handle nested noun groups, pronouns, and other phenomena. A micromodel of the system, giving enough of the structure to enable the reader to observe internal mechanisms in considerable detail, is included. This processor is designed to be transportable to a number of other office automation domains such as calendar management, message-passing, and desk calculation. Various examples and statistics related to its behavior in the text manipulation application are given. The system has been implemented in PASCAL and can run on any machine that supports this language.

A natural language processor is described for control of a machine in task-oriented situations. Particular emphasis is given to issues related to flow-of-control statements in dialogue. These include branching constructs, as in `if row 1 contains a positive entry, then . . .' and looping constructs, as in `repeat for all other rows'. Special problems are discussed concerning the processing of deeply nested control structures, pronoun resolution, and the handling of conjunctions. An experiment is described in which the robustness of the conditional feature was tested with a group of computer naive subjects. It was found that subjects could discover and use the feature effectively in solving problems even though the fact of its existence was systematically withheld during the training session.

A characterization theorem is given for the classes of functions which are representable by signature table systems. The usefulness of the theorem is demonstrated in the analysis and synthesis of such systems. The limitations on the power of these systems come from the restrictions on the table alphabet sizes, and a technique is given for evaluating these limitations. A practical learning system is proposed and analyzed in terms of the theoretical model of this paper. Then an improved method is described and results are presented from a series of experiments.

Production rule schemas are given which hold the basic information necessary for coding recursive loops and branches in LISP. Information from the user concerning the desired program is used to instantiate the schemas to yield production rules, and then these rules generate executable code in a strictly syntactic fashion. Emphasis is placed on decomposing the synthesis problem into a hierarchy of tasks which can each be solved by application of a schema. The method is demonstrated by showing how programs can be synthesized from examples of their input-output behaviors.

A class of LISP programs that is analogous to the finite-state automata is defined, and an algorithm is given for constructing such programs from examples of their input-output behavior. It is shown that the algorithm has robust performance for a wide variety of inputs and that it converges to a solution on the basis of minimum input information.

An autoprogrammer is an interactive computer programming system which automatically constructs computer programs from example computations executed by the user. The example calculations are done in a scratch pad fashion at a computer display using a light pen or other graphic input device, and the system stores a detailed history of all of the steps executed in the process. Then the system automatically synthesizes the shortest possible program which is capable of executing the observed examples. The paper describes the computational environment provided by the system, proves that the program synthesis technique is both "sound" and "complete," describes the design of the system, and gives some programs it was used to create.

An algorithm is given for synthesizing a computer program from a trace of its behavior. Since the algorithm involves a search, the length of time required to do the synthesis of nontrivial programs can be quite large. Techniques are given for preprocessing the trace information to reduce enumeration, for pruning the search using a failure memory technique, and for utilizing multiple traces to the best advantage. The results of numerous tests are given to demonstrate the value of the techniques.

The Nerode realization technique for synthesizing finite-state machines from their associated right-invariant equivalence relations is modified to give a method for synthesizing machines from finite subsets of their input-output behavior. The synthesis procedure includes a parameter that one may adjust to obtain machines that represent the desired behavior with varying degrees of accuracy and that consequently have varying complexities. We discuss some of the uses of the method, including an application to a sequential learning problem.