By studying biological systems, Several definitions for the term robot have been proposed principles may be discovered that can be used, perhaps by (Jablonowski and Posey, 1985). None of these definitions analogy, to improve the functional components of a robot are adequate because they exclude robot intelligence of as well as their cooperation.

Our descriptive vocabulary may still In this article I propose to describe an area of artificial contain proper names as modifiers but the explanatory intelligence (Al) research that I and several colleagues vocabulary now involves the impersonal qualities of an have been enaged in for a number of years.

Artificial intelligence, or AI, is largely an experimental science--at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work.

Automatic computer programming or automatic programming occurs whenever a machine aids in this process. The amount of automatic programming that is occurring is a variable quantity that depends on how much aid the human is given. There are a number of dimensions on which the level of help can be measured including the level of the language used by the human, the amount of informality allowed, the degree to which the system is told what to do rather than how to do it, and the efficiency of the resulting code. Thus we usually say that there is a higher degree of automatic programming whenever a higher level language is used, less precision is required of the human, the input instructions are more declarative and less procedural, and the quality of the object code is better. The technologies of automatic programming thus include the fields that help move the programming experience along any of these dimensions: algorithm synthesis, programming language research, compiler theory, human factors, and others. This paper will concentrate on only the first of these topics, formal methodologies for the automatic construction of algorithms from fragmentary information. The formal methodologiest have been separated into two categories, synthesis from formal specifications and synthesis from examples. In the former case, it is assumed a specification is given for the target program with adequate domain information so that the target program can be derived in a series of logical steps.

This paper will be concerned with the problem of obtaining this performance from the machine by giving it examples of the desired computation and having it program itself. We will be concerned with designing a trainable Turing machine although the concepts presented are applicable in a much more general context as discussed in Section 4. The Turing machine to be discussed here will have an infinite one dimensional tape and will have the capability in one move to read a symbol on the tape, print a new symbol to replace the one just read, and step right or left one increment on the tape. It will have a deterministic finite-state controller with a designated initial state which will upon receiving an input symbol read from the tape, yield the symbol to be printed and the step direction (right or left) to be made.

Questions are asked and answered every day. Question answering (QA) technology aims to deliver the same facility online. It goes further than the more familiar search based on keywords (as in Google, Yahoo, and other search engines), in attempting to recognize what a question expresses and to respond with an actual answer. First, questions do not often translate into a simple list of keywords. For example, the question (1) Which countries did the pope visit in the 1960s? A much more complex set of keywords is needed in order to get anywhere close to the intended result, and experience shows that people will not learn how to formulate and use such sets. Second, QA takes responsibility for providing answers, rather than a searchable list of links to potentially relevant documents (web pages), highlighted by snippets of text that show how the query matched the documents. While this is not much of a burden when the answer appears in a snippet and further document access is unnecessary, QA technology aims to move this from being an accidental property of search to its focus. In keyword search and in much work to date on QA technology, the information seeking process has been seen as a one-shot affair: the user asks a question, and the system provides a satisfactory response. However, early work on QA (Section 1.1) did not make this assumption, and newly targeted applications are hindered by it: while a user may try to formulate a question whose answer is the information Question Answering 631 they want, they will not know whether they have succeeded until something has been returned for examination. If what is returned is unsatisfactory or, while not the answer, is still of interest, a user needs to be able to ask further questions that are understood in the context of the previous ones. For these target applications, QA must be part of a collaborative search process (Section 3.3). In the rest of this section, we give some historical background on QA systems (Section 1.1), on dialogue systems in which QA has played a significant role (Section 1.2), and on a particular QA task that has been a major driver of the field over the past 8 years (Section 1.3). Section 2 describes the current state of the art in QA systems, organized around the de facto architecture of such systems. Section 3 discusses some current directions in which QA is moving, including the development of interactive QA.

It is no pure coincidence that an organic chemist rather than a computer specialist is writing this foreword.

Kernel ridge regression (KRR) is a standard method for performing non-parametric regression over reproducing kernel Hilbert spaces. Given $n$ samples, the time and space complexity of computing the KRR estimate scale as $\mathcal{O}(n^3)$ and $\mathcal{O}(n^2)$ respectively, and so is prohibitive in many cases. We propose approximations of KRR based on $m$-dimensional randomized sketches of the kernel matrix, and study how small the projection dimension $m$ can be chosen while still preserving minimax optimality of the approximate KRR estimate. For various classes of randomized sketches, including those based on Gaussian and randomized Hadamard matrices, we prove that it suffices to choose the sketch dimension $m$ proportional to the statistical dimension (modulo logarithmic factors). Thus, we obtain fast and minimax optimal approximations to the KRR estimate for non-parametric regression.

The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assumptions. For completeness, NSS is evaluated through experiments on various simulated and real data sets, in comparison with some other linear model based classifiers. It is also shown that NSS can obtain effective classification results and is very efficient, especially for large scale data sets.

In many applications one may acquire a composition of several signals that may be corrupted by noise, and it is a challenging problem to reliably separate the components from one another without sacrificing significant details. Adding to the challenge, in a compressive sensing framework, one is given only an undersampled set of linear projections of the composite signal. In this paper, we propose using the Dantzig selector model incorporating an overcomplete dictionary to separate a noisy undersampled collection of composite signals, and present an algorithm to efficiently solve the model. The Dantzig selector is a statistical approach to finding a solution to a noisy linear regression problem by minimizing the $\ell_1$ norm of candidate coefficient vectors while constraining the scope of the residuals. If the underlying coefficient vector is sparse, then the Dantzig selector performs well in the recovery and separation of the unknown composite signal. In the following, we propose a proximity operator based algorithm to recover and separate unknown noisy undersampled composite signals through the Dantzig selector. We present numerical simulations comparing the proposed algorithm with the competing Alternating Direction Method, and the proposed algorithm is found to be faster, while producing similar quality results. Additionally, we demonstrate the utility of the proposed algorithm in several experiments by applying it in various domain applications including the recovery of complex-valued coefficient vectors, the removal of impulse noise from smooth signals, and the separation and classification of a composition of handwritten digits.